Biomarker signature method, and apparatus and kits therefor

ABSTRACT

The present invention discloses methods, kits, and apparatus as well as reagents and compositions associated therewith for deriving an indicator for use in diagnosing the presence, absence or degree of at least one condition in a biological subject or in prognosing at least one condition in a biological subject. Also disclosed is a biomarker signature for use in diagnosing the presence, absence or degree of at least one condition in a biological subject or in prognosing at least one condition in a biological subject. The present invention further discloses methods, kits and apparatus, as well as reagents and compositions associated therewith, for identifying biomarkers for use in a biomarker signature.

This application is a divisional of U.S. application Ser. No. 14/616,565 entitled “Biomarker signature method, and apparatus and kits therefor” filed Feb. 6, 2015, which claims priority to Australian Provisional Application No. 2014900363 entitled “Biomarker signature method, and apparatus and kits therefor” filed 6 Feb. 2014, the entire contents of which are incorporated herein by reference.

STATEMENT REGARDING SEQUENCE LISTING

The Sequence Listing associated with this application is provided in text format in lieu of a paper copy, and is hereby incorporated by reference into the specification. The name of the text file containing the Sequence Listing is DAVI_035_02US_ST25.txt. The text file is 4.4 MB, was created on May 15, 2015, and is being submitted electronically via EFS-Web.

BACKGROUND OF THE INVENTION

The present invention relates to method, kit and apparatus and to reagents and compositions associated therewith for deriving an indicator for use in diagnosing the presence, absence or degree of at least one condition in a biological subject or in prognosing at least one condition in a biological subject, to a biomarker signature for use in diagnosing the presence, absence or degree of at least one condition in a biological subject or in prognosing at least one condition in a biological subject, and to a method, kit and apparatus, as well as reagents and compositions associated therewith, for identifying biomarkers for use in a biomarker signature.

DESCRIPTION OF THE PRIOR ART

The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that the prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavor to which this specification relates.

The analysis of gene expression products for diagnostic purposes is known. Such analysis requires identification of one or more genes that can be used to generate a signature for use in distinguishing between different conditions. However, such identification can require the analysis of many gene expression products, which can be mathematically complex, computationally expensive and hence difficult. Much of the biomarker discovery process is devoted to identifying a subset of the data that may have relevant import, from which a signature is derived using a combination of these values to produce a model for diagnostic or prognostic use.

WO2004/044236 describes a method of determining the status of a subject. In particular, this is achieved by obtaining subject data including respective values for each of a number of parameters, the parameter values being indicative of the current biological status of the subject. The subject data are compared to predetermined data that includes values for at least some of the parameters and an indication of the condition. The status of the subject, and in particular, the presence and/or absence of the one or more conditions, can then be determined in accordance with the results of the comparison.

US2010/0028876 describes methods for diagnosing biological states or conditions based on ratios of gene expression data from cell or tissue samples, such as cancer cell or tissue samples, by differentiating between cell types, including cancer cell types. The invention provides sets of genes that are expressed differentially in normal and cancer lung cells and tissues to be able to differentiate these cells and tissues. Such cellular differentiation is important in diagnosing cancer and cancer types. The sets of genes are identified by the degree (fold change) of up or down regulation. These sets of genes can be used to discriminate between normal and malignant cells or tissues, and between classes of malignant cells or tissues. Accordingly, diagnostic assays for classification of tumors, prediction of tumor outcome, selecting and monitoring treatment regimens and monitoring tumor progression/regression also are provided.

However, traditional methods for biomarker identification and traditional combinations of biomarkers use a relatively large number of biomarkers, which in turn makes tests expensive to perform, limiting their use in practice. In addition, the prior art does not describe the use of immune system biomarker ratios, or a method of identifying minimal sets of immune system biomarker ratios useful in determining the presence, absence, degree or prognosis of immune system-mediated medical conditions.

SUMMARY OF THE PRESENT INVENTION

In one broad form the present invention seeks to provide a method for determining an indicator used in assessing a likelihood of a biological subject having a presence, absence, degree or prognosis of at least one medical condition, the method including:

-   -   a) determining a pair of biomarker values, each biomarker value         being a value measured or derived for at least one corresponding         immune system biomarker of the biological subject and being at         least partially indicative of a concentration of the immune         system biomarker in a sample taken from the subject;     -   b) determining a derived biomarker value using the pair of         biomarker values, the derived biomarker value being indicative         of a ratio of concentrations of the pair of immune system         biomarkers; and,     -   c) determining the indicator using the derived biomarker value.

Typically the method includes:

-   -   a) determining a first derived biomarker value using a first         pair of biomarker values, the first derived biomarker value         being indicative of a ratio of concentrations of first and         second immune system biomarkers;     -   b) determining a second derived biomarker value using a second         pair of biomarker values, the second derived biomarker value         being indicative of a ratio of concentrations of third and         fourth immune system biomarkers; and,     -   c) determining the indicator by combining the first and second         derived biomarker values.

Typically the method includes combining the derived biomarker values using a combining function, the combining function being at least one of:

-   -   a) an additive model;     -   b) a linear model;     -   c) a support vector machine;     -   d) a neural network model;     -   e) a random forest model;     -   f) a regression model;     -   g) a genetic algorithm;     -   h) an annealing algorithm;     -   i) a weighted sum;     -   j) a nearest neighbor model; and,     -   k) a probabilistic model.

Typically the method is performed at least in part using an electronic processing device.

Typically the method includes, in at least one electronic processing device:

-   -   a) obtaining at least two pairs of measured biomarker values,         each measured biomarker value being a measured value of a         corresponding immune system biomarker of the biological subject;     -   b) determining a first derived biomarker value indicative of a         ratio of concentrations of first and second immune system         biomarkers;     -   c) determining a second derived biomarker value indicative of a         ratio of third and fourth immune system biomarkers; and,     -   d) determining the indicator by combining the first and second         derived biomarker values.

Typically the method includes, in at least one processing device, generating a representation of the indicator.

Typically the representation includes:

-   -   a) an alphanumeric indication of the indicator;     -   b) a graphical indication of a comparison of the indicator to         one or more indicator references;     -   c) an alphanumeric indication of a likelihood of the subject         having at least one medical condition.

Typically the method includes:

-   -   a) comparing the indicator to an indicator reference; and,     -   b) determining a likelihood in accordance with results of the         comparison.

Typically the indicator reference is based on at least one of:

-   -   a) an indicator threshold range;     -   b) an indicator threshold; and,     -   c) an indicator distribution.

Typically the indicator reference is derived from indicators determined for a number of individuals in a reference population.

Typically the indicator reference is based on a distribution of indicators determined for a group of a reference population, the group consisting of individuals diagnosed as having the medical condition or lacking the medical condition.

Typically the reference population includes:

-   -   a) a plurality of individuals of different sexes;     -   b) a plurality of individuals of different ethnicities;     -   c) a plurality of healthy individuals;     -   d) a plurality of individuals suffering from at least one         diagnosed medical condition;     -   e) a plurality of individuals lacking the at least one diagnosed         medical condition;     -   f) a plurality of individuals showing clinical signs of at least         one medical condition;     -   g) first and second groups of individuals, each group of         individuals suffering from a respective diagnosed medical         condition; and,     -   h) first and second groups of individuals, the first group of         individuals suffering from a diagnosed medical condition, and         the second group lacking the diagnosed medical condition.

Typically the indicator is for use in determining the likelihood that a biological subject has at least one medical condition, and wherein the reference population includes:

-   -   a) individuals presenting with clinical signs of the at least         one medical condition;     -   b) individuals diagnosed as having the at least one medical         condition;     -   c) individuals diagnosed as lacking the at least one medical         condition; and,     -   d) healthy individuals.

Typically the indicator reference is retrieved from a database.

Typically the likelihood is based on a probability generated using the results of the comparison.

Typically the indicator is for determining a likelihood of the subject having a first or second condition, and wherein the method includes:

-   -   a) comparing the indicator to first and second indicator         references, the first and second indicator references being         indicative of first and second conditions; and,     -   b) determining the likelihood in accordance with the results of         the comparison.

Typically the method includes:

-   -   a) determining first and second indicator probabilities using         the results of the comparisons; and,     -   b) combining the first and second indicator probabilities to         determine a condition probability indicative of the likelihood.

Typically the first and second indicator references are distributions of indicators determined for first and second groups of a reference population, the first and second group consisting of individuals diagnosed with the first or second condition respectively.

Typically the method includes:

-   -   a) obtaining a sample taken from the biological subject, the         sample including polynucleotide expression products; and,     -   b) quantifying at least some of the polynucleotide expression         products within the sample to determine the pair of biomarker         values.

Typically the method includes, determining the indicator at least in part using a ratio of concentrations of the polynucleotide expression products.

Typically the method includes:

-   -   a) quantifying polynucleotide expression products by:     -   b) amplifying at least some polynucleotide expression products         in the sample; and,     -   c) determining an amplification amount representing a degree of         amplification required to obtain a defined level of each of a         pair of polynucleotide expression products; and,     -   d) determining the indicator by determining a difference between         the amplification amounts.

Typically the amplification amount is at least one of:

-   -   a) a cycle time;     -   b) a number of cycles;     -   c) a cycle threshold;     -   d) an amplification time; and,     -   e) relative to an amplification amount of another amplified         product.

Typically the method includes determining:

-   -   a) a first derived biomarker value by determining a difference         between the amplification amounts of a first pair of         polynucleotide expression products;     -   b) a second derived biomarker value by determining a difference         between the amplification amounts of a second pair of         polynucleotide expression products;     -   c) determining the indicator by adding the first and second         derived biomarker values.

Typically the immune system biomarker is a biomarker of an immune system of the biological subject that is altered, or whose level of expression is altered, as part of an inflammatory response to damage or pathogenic insult.

Typically:

-   -   a) the at least two immune system biomarkers have a mutual         correlation in respect of the at least one condition that lies         within a mutual correlation range, the mutual correlation range         being between ±0.9; and,     -   b) the indicator has a performance value greater than or equal         to a performance threshold representing the ability of the         indicator to diagnose the presence, absence, degree or prognosis         of the at least one condition, the performance threshold being         indicative of an explained variance of at least 0.3.

Typically the mutual correlation range is at least one of:

-   -   a) ±0.8;     -   b) ±0.7;     -   c) ±0.6;     -   d) ±0.5;     -   e) ±0.4;     -   f) ±0.3;     -   g) ±0.2; and,     -   h) ±0.1.

Typically each immune system biomarker has a condition correlation with the presence, absence, degree or prognosis of the at least one condition that lies outside a condition correlation range, the condition correlation range being between ±0.3.

Typically the condition correlation range is at least one of:

-   -   a) ±0.9;     -   b) ±0.8;     -   c) ±0.7;     -   d) ±0.6;     -   e) ±0.5; and,     -   f) ±0.4.

Typically the performance threshold is indicative of an explained variance of at least one of:

-   -   a) 0.4;     -   b) 0.5;     -   c) 0.6;     -   d) 0.7;     -   e) 0.8; and,     -   f) 0.9.

Typically the immune system biomarker value is indicative of a level or abundance of a molecule selected from one or more of a nucleic acid molecule and a proteinaceous molecule.

In some embodiments, the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein the method includes:

-   -   a) determining a first pair of biomarker values indicative of a         concentration of polynucleotide expression products of the         PLA2G7 gene and PLAC8 gene;     -   b) determining a second pair of biomarker values indicative of a         concentration of polynucleotide expression products of the         CEACAM4 gene and LAMP1 gene; and,     -   c) determining the indicator using the first and second pairs of         biomarker values.

In some embodiments, the indicator is for determining a likelihood of the subject having inSIRS or a healthy condition, and wherein biomarker values are determined from at least one inflammatory response syndrome (IRS) immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

-   -   a) the first IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: NUMB, RAB27A,         USP3//LOC100130855, HIF1A, LBXCOR1//PIAS1//CALML4, SQRDL,         C20orf74, IL10RB, PARP4, DNTTIP1, MTMR6//LOC646482, LAMP2,         MAPK1, SERINC3//TTPAL, IGSF6//METTL9, RP2, C18orf32, LOC284757,         MTMR10//MTMR15, SLC12A6, LCP1, CHP, PRR13, C20orf177, ZFP106,         DICER1, PHF12, IFNAR1, BNIP2, UBE2A, NIN, MBD2//SNORA37, TM9SF2,         RAB8B, CLIP1, WAS, DNAJC3, CDADC1, KIAA0317, MED13L, INTS6,         PDK3, MYO5A, NUPL1, VEZF1, CUL4B, USP9X//USP9Y, RPS6KA3,         IL17RA//CECR7, ELF1, TMX4, TAOK1, ELMO2, STAT5B//STAT5A,         PAN3//EEF1A1//CHCHD2, SIPA1L1//SNORD56B//LOC145474//LOC283567,         OSBPL1A, SYNJ1, U2AF1, NPEPPS//TBC1D3F//LOC440434, AP1G1, SNTB2,         ZNF230//ZNF222, LYRM1, ME2, GALNT1, DYRK1A, ZMYM2, ARID4A, TOB1,         DOCK11, ACTR10, ZMYM5//ZMYM2, FNDC3A, NUFIP2, STRADA,         SPG11//ISLR, SPATA13//C1QTNF9, BRWD3, BACH1, CLTC, LIG4,         C21orf41//BACH1, KPNB1, DHRS7, USP8, LACTB, SYNE2,         ZDHHC20//LOC728099, EAPP, MED13//LOC100129112, TAOK3, NLGN3,         CIT, RIPK3, CP110, ABHD2, GNA13, GGNBP2, PXN and PTPN1         (hereafter referred to interchangeably herein as “group A IRS         immune system biomarker genes” or “group A IRS biomarker         genes”); and     -   b) the second IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: AGFG1, BMX//HNRPDL,         MCM5, TRIM28, GRWD1, ZNF574, ARRDC2, PELP1, SHPK, GPS1, FAM38A,         FBXO31, C16orf58//LOC100128371, NLRC3, JMJD8, CDK10, TRAPPC2L,         PRMT7, BRF1, MTA1/LOC647310//LOC100128343, PLD4, DDX54//CCDC42B,         PLBD1, IRAK3, FGD4, ARG1, RANGAP1, UNC84B, SAMSN1//LOC388813,         PFKL, S100A12, KIF22, LRRN1, CCDC134, LZTR1, GZMM, ICAM2, TMC8,         LAT//SPNS1//NPIPL2//LOC728741//LOC730153//NPIPL3//SPIN1//LOC728888//LOC100289169//LOC728734//LOC729602//LOC100288442//LOC100288332,         CLEC4D, CDK5RAP1, PPP1R16B, DAZAP1, LMF1, EDC4,         IL21R//LOC283888, JMJD7-PLA2G4B//JMJD7//PLA2G4B, TMEM120B//RHOF,         ENTPDH/C10orf131, ACSL1, ZC3H7B, CHERP//C19orf44//CALR3, U2AF2,         PYGL, SOS2, ANKRD22, MEGF9, MGAM//LOC100124692, IL1R2, IL2RB,         FCAR, IL27RA, DHX37, PATZ1, PRDM15, NOSIP, RPTOR, SPG7, DNAJA3,         VNN1, SEPT9, THAP11, LPCAT2, MAN2C1, PITPNM2,         NOC2L//SAMD11//LOC401010, ZMYM3, FTSJ1, KCNE1, ACTR5, FAM110A,         FAM134C, LLGL2, INF2, KDM2B, ACSL4, B4GALT5, CD79B, BCL11B,         ERLIN1, TLR4, EVL, SRGN, SLC37A3, GPR141, MSL3,         MMP9//LOC100128028, MAP2K6, PHTF1, KLHL36, POLR3E, PCNX, PNKP,         TMEM104, TRPV2, SEPT1, APH1B, POLE, MED24, MPI, C12orf49, PES1,         ERCC1//CD3EAP, CD177, CPD, MEF2A//LYSMD4, C14orf43, RPLP0,         CDC25B, SYMPK, ARHGEF18//LOC100128573, PSTPIP2,         HERC2//HERC2P2//HERC2P3//LOC440248, MAPK14, F5, PLCG1, ZNF416,         AARS, KLHL2, APOBEC3A//APOBEC3B, CMTM1/CKLF, USP11,         MAP3K14//LOC100133991, GOLGA3, TMEM204, S100A8, IL1R1, DHPS,         PPP2R1A, UBTF, DRG2, DNMT1, USP36, ZBTB4, TSC2, KIAA0195,         KIAA0182, ALOX5AP, TGIF2, ST20//C15orf37, FN3KRP, ABCD4, ZFP64,         NEO1, PPIL2//YPEL1, RNPS1, NF2, SERPINB1, DDX51, PRPF6, TIMM22,         SYS1//SYS1-DBNDD2//DBNDD2, RAB31, KRI1, SMARCA4, CLUAP1,         C16orf67, C20orf4, CHTF8//HAS3, NPTN, CSRP2BP, AES, ODZ1,         MTMR15//MTMR10, SIRPD, EEF2//SNORD37, DKC1/SNORA36A//SNORA56,         CEACAM4, C12orf43, RANBP3, EEF2K, LOC338799, PLP2, AKAP8, ELAC2,         AKAP1, TBC1D4, ALOX5, WSB1, BAZ1A, ETS2, GGA2, CSTF2//RAD21,         METTL9, GYG1, CRAMP1L//HN1L, EVI2B, PPP1R13B, POP5, C20orf3,         WDR59, KCNJ15, PGLYRP1, ELAVL1, SLC25A1, PSMD3, CDC42EP3, FTSJ3,         C2CD2, RBM19, CDH26, TRMT2B, GTF2F1//LOC100130856,         SNRPN//SNURF//IPW//SNORD116-16//SNORD116-18//SNORD116-21//SNORD116-22//SNORD116-17//SNORD116-19//PAR5//PAR-SN//SNORD116-2//SNORD116-25//SNORD116-26//SNORD107//SNORD115-12//SNORD115-5//SNORD115-6//SNORD115-9//SNORD116-11//SNORD116-12//SNORD116-13//SNORD116-28//SNORD116-4//SNORD64//PAR1//SNORD109A//SNORD109B//SNORD116-6//SNORD116-3//SNORD116-9//SNORD115-13//SNORD115-1//SNORD115-14//SNORD115-15//SNORD115-21//SNORD115-10//SNORD115-7//SNORD115-16//SNORD115-40//SNORD115-42//SNORD115-11//SNORD115-29//SNORD115-34//SNORD115-36//SNORD115-4//SNORD115-43//HBII-52-24//SNORD116-5//SNORD116-7//SNORD115-26//SNORD115-30//SNORD116-15//SNORD116-8//SNORD115-2//SNORD115-39//SNORD116-14//SNORD116-20//SNORD115-8//SNORD115-3//SNORD115-38//SNORD115-41//SNORD115-22//SNORD115-44//SNORD116-1//SNORD115-17//SNORD115-18//SNORD115-19//SNORD115-20//SNORD116@,         SLC9A8, RPA1, ADARB1, AFG3L2, MCTP2, DACH1, SEH1L, RRP1B,         ZNF335, WDR73, TAF15, MOSPD2, WIPIMARSG, ARRB2, PLIN5ARG1,         SNRPD3//C22orf13, CTNNBL1, ZNF175, NCF4, DDX27, FBXO21, TDP1,         ATXN2L, ILF3, VAPA, DDX19B//DDX19A, NCOR2, KL, MTHFS,         TOM1L2//LOC246315, APOBEC3D, EXD2,         CDR2//RRN3//LOC100131998//LOC653390, ADCY4, DHX33, CKLF, GTF3C1,         PRKCSH, DHX35, HSPH1, CCDC92, BCOR, CCPG1//PIGB//DYX1C1, MCM3AP,         FPR1, ZNF460, AKAP8L, DCAF7, RNF24, NSMCE1, PDHA1, SAFB2//SAFB,         ITM2B, ZNF236, PI4KA//PI4KAP1//PI4KAP2//LOC100293141, CSTB,         C14orf138, ITGAL, ARID3A, COG7, TYROBP, HP//HPR, SRCAP//SNORA30,         COG1, GK//GK3P//FTL//LOC652904, C15orf63//SERF2, SERPINA6,         SMG6//C17orf6, INO80, C16orf62, RAB35, PEF1, C14orf101,         TMEM185A, LIMK2, CTCF, DIABLO//B3GNT4, VPS33A, UNQ1887,         TBCB//POLR2I, ABHD13, SLC24A6, EDNRB, CA12, ANAPC5, TMC3,         TRIAP1, ABHD12B, TDRD9, EIF2B1, CXorf59,         LRRC37A3//LRRC37A2//LRRC37A//ARL17P1//LRRC37A4//LOC100294335//LO         C644397, SDS, SYCP2, TBC1D8B, TMEM31, GUCY1B2, PFN1, SLC24A3,         ABCC11//LONP2 and ZNF257//ZNF492//ZNF99//ZNF98//LOC646864         (hereafter referred to interchangeably herein as “group B IRS         immune system biomarker genes” or “group B IRS biomarker         genes”).

In some embodiments, the indicator is for determining a likelihood of the subject having ipSIRS or a healthy condition, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

-   -   a) the first IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: LTBP3, LPHN1,         NR1D1//THRA, METTL9, PLD4, MAPK14, FAM102A, MYBBP1A//SPNS2,         FLJ10232, SEMA4C, LMF1, PLBD1, MAN1C1, B4GALT5, ENGASE, NDRG2,         TLR5, WDR4, PATZ1, CD177, LILRA5, SIRPD, ADAMTS10, TCF7, GGT7,         GYG1, CDAN1, BRF1, GPR84, TMC6//LOC100131096, PGAP3,         GRAP//SNORD3B-1//SNORD3B-2//LOC400581, SHPK, NOG,         PVRIG//PILRB//STAG3, TDRD9, TMC8, C14orf21, NLRC4, APBA2, TBL3,         LDOC1L, C16orf58//LOC100128371, KLHL3, IRAK3,         JMJD7-PLA2G4B//JMJD7//PLA2G4B, RAB31, IL1 ORB,         NFATC1//LOC100127994, S100A12, SLC2A3//SLC2A14, GPA33, PLXDC1,         BCL6//LOC100131635, GTPBP3, SQRDL, CD7, TMEM177//LOC100125918,         FBXO31, CACNA1E, LILRA4, FAM38A, UBE2J1, SBF1//SBF1P1, TMEM204,         FCAR, CDK4/MARCH9/C3HC4, ZBTB4, PIWIL4, RNASE2//LOC643332,         PRMT7, AGFG1, CMTM1/CKLF, MCTP1, ARL11, FMNL3//PRPF40B, FAM151B,         BAHD1, OSBPL7, ZAP70, TUBGCP6, MAP2K6, NPTN, C3AR1, CD247,         S100A8, TBCD, LMNB1/PCIF1, PFKL, GRWD1, PYGL, UPP1, OMG,         SAMSN1/LOC388813, BLCAP, PTPRS, FAM20A, CARD6, SPPL2B, IL2RB,         SORT1, BST1, TAF1C//ADAD2, SEMA4F, NCAPH2, MCTP2, ZAK, CCR7,         MAN2C1, NEURL4//GPS2//D4S234E, BMX//HNRPDL, TRAPPC6A, LPCAT2,         C19orf60, SLC4A10, C14orf101, TP53I3, IL1RN, AIM2, UBE2R2, PNKP,         ZNF70, SEPT1, NEO1, MPRIP, DPH1//OVCA2, C16orf67, CD58, RAB27A,         EEF2K, CLIC1, MBLAC2, IFNGR2, CRTC1//MAML2, CACNB1, GALNT3,         C19orf6, C20orf74, RALB, GPRASP1, CA4, ETS2, RP2, MARS2, RAB32,         FAIM3, C20orf24//SLA2, ZNF549, PIGL, PHTF1, IL18R1, IPO4,         ZFP106, SLC12A4, DNTTIP1, S100A11, ZNF544, ATXN1, GNLY, MID2,         BACH2, INF2, ARFGAP1, MSL3, SOS2, ARL8A, PTPRJ//OR4B1, NAT9,         RHOT2//FBXL16, PNPLA1, DNAJC13, GNG5, FAM129C, PXK, C10orf119,         BATF, LMO7, KLF2, NRD1, CLCN7, GLA, CFLAR, SYCP2, IMAGE5303689,         LPGAT1, PTGDR, LAMP2, ZNF607, INSL3//JAK3, DUSP3, PCNX, CD79B,         IRAK1, ZNF550//ZNF549, LOC100130950, SPTLC2, CTSA, RAP2C, ADCY9,         MED12L, MTHFD2, CAP1, TOR1AIP2//TOR1AIP1//IFRG15, CHP, TSEN2,         LYRM1, UBE2A, NUPL1, YIPF1, FRMD3, KAL1, CLTC, FLVCR2//RPS24,         WSB2, KIAA0040, JAG1, GPR183, N6AMT1, ZNF563, AP3B2,         SERPINB2//SERPINB10, CDH2, ITFG1, EDEM2, RNF135, HPSE, DSC1,         FOXN2, RASSF2, ZNF420, ZFP28, UBE2G2//SUMO3, PTTG11P, PRKCB,         KIT, PLEK, MAP4K4, GBA//GBAP, CLIP1, EDEM3, SERINC3//TTPAL,         TPST2, HNRPLL, TP53BP2, KCND1, GCLM, RIT1, OSCAR, DDX59, EDNRB,         ELMO2, RRAGC, AFTPH, DCUN1D3//LYRM1, RSBN1, IFI30, SNX1, PTPN1,         SEP15, ARMCX5, ALAS1, NFKBIA, STXBP2//L00554363//LOC100131801,         C15orf24, SRI, ASGR2, NSF//LOC728806, TRIM69, SEC23A, PLAUR,         RAB3GAP2//AURKAPS1/AURKA//SNORA36B, MAOB//NAT13, DEDD, SEC23B,         COPA, EGF, STRADA, SIAE, C5, SLC30A1, ANXA4, NKG7, ABHD12B,         TESK2, LONRF3, PIKFYVE, SH3BGRL3, ARMCX3, NEU1, SPAST,         STX6//KIAA1614, TADA3L, LIN37//PSENEN, UBR3, WDR90, RTN2 and         TMUB2 (hereafter referred interchangeably herein as “group C IRS         immune system biomarker genes” or “group C IRS biomarker         genes”); and,     -   b) the second IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: CNNM3, SLC25A45,         UNC84B, ARHGEF18//LOC100128573, PIK3IP1, EPHX2, SEPT9, ITPKB,         TSPYL2//GPR173, GALT, USP11, CBX7//LOC100128400,         MAP3K14//LOC100133991, CDK5RAP1, KLHL36, SPG7, ZNF574, RASA3,         KLHL22//KRT18, PYGO2, USP36, LCK, SKI, C5orf45//SQSTM1,         PIK3C2B//LOC100130573, ANGEL1, ZCCHC14, CIRBP, ZMIZ2,         TMEM120B//RHOF, NOC2L//SAMD11//LOC401010, PPP1R13B, ZNF416,         PBXIP1, SMYD5//NOTO, ZNF529, EDC4, LENG8,         TBC1D22A//LOC100289878, CORO7, COG8//PDF, CUL9, RASGRF2,         CHERP//C19orf44//CALR3, POLR3E, CNNM4, TSC2,         XYLT2//LOC100130580, TP53, LMBR1L, AKT1, SLC7A6//SLC7A6OS,         LDLRAP1, SGSM2, ZNF764//ZNF747, AKAP1, RNPS1, ICAM2, KIF3A,         TGIF2, VAC14//LOC100130894, CXXC5, DCAF15, TARBP1, RCAN3, IMP4,         LUC7L, SIN3B, TRMT2B, POFUT2, AXIN1, PPIL2//YPEL1,         NLRP1//LOC728392, TMEM63A, TMEM208, ZNF362, GNG7, CASS4, ZNF287,         DGKE, CEP68, ASXL1, CLUAP1, WDR89, CD40LG, MGC57346//C17orf69,         PPIE//CCDC25, FCHO1, TNPO2//SNORD41, CSNK1E//LOC400927, CLYBL,         XAB2, METT10D//LOC284009, PRPF6, AKAP8, SH3BP1//PDXP,         TOM1L2//LOC246315, ZNF329, ZNF274, FAM119A, SMYD3,         UNC84A//C7orf20, ZNF256, PSD4, CCDC130, LIMD2//MAP3K3, ELP2,         ZNF8, AFG3L2, TXK, DDX27, FBXL12, TNRC6C, TADA1L,         KIAA0355//FLJ21369, ZNF211, ZNF808//ZNF578//ZNF611,         SRCAP//SNORA30, BANP//RUNDC2C, ADARB1, CCDC71, KTI12, TCF25,         XYLT1/LYRM2//ZC3H11A, DET1, ABCF3, PRKCZ, KIAA0141, CHI3L1,         RPGRIP1, TTC31, MTMR15//MTMR10, MEF2D, TMEM50B, GLOD4, PRPF8,         C14orf43, P2RX5, MSH2, PCCA, DENND4B, SLC43A2, MAPK8IP3,         TUBGCP5, C19orf2, SEH1L, CCDC104, TRIM62, TDRKH, COG1, POLR1B,         AFG3L1, TYK2, RBM3, UBTF,         RP11-94I2.2//NBPF16//NBPF11//NBPF15//NBPF8//NBPF20//NBPF10//NBPF14//NBPF1//LOC100288142//NBPF12//KIAA1245//LOC100290137,         ZNF41, ZNF461, PI4KA//P14KAP1//PI4KAP2//LOC100293141, THEM4,         BCL11A, CC2D1B, WDR73, BBS2//OGFOD1,         RRN3//LOC653390//LOC730092//LOC100131998, NOP58, NUCKS1, ZNHIT6,         RXRB, AKT3, FANCM, ERN1, FAM117B, COX11//TOM1L1, ACVR2A,         RP3-402G11.5, AHCTF1, CLN8, NVL, SAPS2, DPEP3, PDE3B, DPEP2,         GGA1, CCDC50,         SNRPN//SNURF//IPW//SNORD116-16//SNORD116-18//SNORD116-21//SNORD116-22//SNORD116-17//SNORD116-19//PAR5//PAR-SN//SNORD116-2//SNORD116-25//SNORD116-26//SNORD107//SNORD115-12//SNORD115-5//SNORD115-6//SNORD115-9//SNORD116-11//SNORD116-12//SNORD116-13//SNORD116-28//SNORD116-4//SNORD64//PAR1//SNORD109A//SNORD109B//SNORD116-6//SNORD116-3//SNORD116-9//SNORD115-13//SNORD115-1//SNORD115-14//SNORD115-15//SNORD115-21//SNORD115-10//SNORD115-7//SNORD115-16//SNORD115-40//SNORD115-42//SNORD115-11//SNORD115-29//SNORD115-34//SNORD115-36//SNORD115-4//SNORD115-43//HBII-52-24//SNORD116-5//SNORD116-7//SNORD115-26//SNORD115-30//SNORD116-15//SNORD116-8//SNORD115-2//SNORD115-39//SNORD116-14//SNORD116-20//SNORD115-8//SNORD115-3//SNORD115-38//SNORD115-41//SNORD115-22//SNORD115-44//SNORD116-1//SNORD115-17//SNORD115-18//SNORD115-19//SNORD115-20//SNORD116@,         C17orf65//ASB16, ZNF317, SNRNP200, CXorf26, MTBP,         NOL11//SNORA38B, CCNL2, ALDOC, PITPNC1, FASTKD2, ZZZ3, PIK3R5,         WDR82, GLDN, CHML, C15orf40, DIDO1, CLCC1//GPSM2//C1 orf62,         SLC35D1, SCRN1, C15orf63//SERF2, ZNF460, SAFB2//SAFB, C16orf54,         DDX18, CTPS2, ZNF382, ZNF101, LIPT1//MRPL30, ITGA6, KIF21B,         INPP5B, SF3A1//CCDC157, ODF2L, NUAK2, CHCHD5, AHSA2//USP34,         YLPM1, TERF2, ZNF830, MAN2B1//MORG1, GPATCH8, SHC1, SEPT4,         SFRS2, TMC3, OTUD5, NARG1L, MKL1//KIAA1659, YTHDF1, SLC14A2,         GGA3 and EXOC7 (hereafter referred to interchangeably herein as         “group D IRS immune system biomarker genes” or “group D IRS         biomarker genes”).

In some embodiments, the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

-   -   a) the first IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: ALKBH5//FLJ13773,         RPS19BP1, RFXANK//MEF2B//LOC729991, NA, CDC6, C19orf56, NA,         ABCC2, THAP11, RTN2, MAZ, TAX1BP3, NUTF2, MPZL3, FBXW5,         HIST1H2BM, CETP, PQLC1, H2AFX, KIAA0101//CSNK1G1, STK17B,         SMARCD3, LOC100134934//CDK3, LPCAT4, LPP, MPZL2, ANKRD9,         PRR13//PCBP2, MDS2, RBM33, GATAD2B//PLIN2, PPTC7, MYBL2, OIP5,         PLA2G7, CRIPT, RNF186, CCDC125, TLE3, C3orf35, SAP130, MXD1,         ZHX2, CDK5RAP3, ENTPD1//C10orf131, NDUFB7, POGZ, DOK3, MCM7,         IL17F, CLPS and DUSP11 (hereafter referred to interchangeably         herein as “group E IRS immune system biomarker genes” or “group         E IRS biomarker genes”); and,     -   b) the second IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: PIWIL4, C11orf82,         ACRC, CLPTM1L, MAGED2, PLACE, ZDHHC4, OTX1, INSIG1, BATF,         MFSD11, DNASE1L1//RPL10, C15orf24, CDS2, KEAP1, ARD1A, POLR2G,         AHCY, SLC39A9, CUGBP1, FAM96B, TM7SF3, CTSZ, CD63, SPPL2A,         ST3GAL2, SEC13, TM9SF1, IRAK2, GOSR2, ADIPOR2, TG, GABRR2,         TPST2, DERL2, CCDC101//LOC388242, VWA5A//OR10D1P, CD300A,         MRPS34, PSMA7, MAPK6, JKAMP, TLR10, RAG1AP1, NEU1, SLC30A1,         PDGFC, ATOX1, CYBASC3, TMEM205//hCG_29977, FAM108B1, ACSS2,         HIST1H4L, AGTRAP, RNF114, UBQLN2, EDF1, C20orf197, UBE2E1, RER1,         ANKRD10, SEC22C, TM2D3, SLC15A2, TRIM28, COX15, CCDC109A, CSTF1,         AIP, ACTR1A, HIST1H4I, YIF1B, TSPAN31, VPS26B, CNIH, TGFBR1,         NIPA2//CYFIP1, DDAH2, BID, CYB5R1, CEACAM4, KIT, GAB2, JAG1,         RPGRIP1, VAT1, GNB5//LOC100129973, SSR4//IDH3G, LAMP1, MRPL41,         RUNX2, ITFG1, DNASE1, ZNRD1//NCRNA00171, NLRP1//LOC728392,         STT3A, MGAT4B//SQSTM1, KIAA1257//ACAD9//LOC100132731, KLHL6,         PTPN6, GALK2, DAD1//OR6J1, PDLIM5, TMEM147, TRAM1//LOC286190,         LZTR1, TNPO1, ACSL5, C22orf37, PLK1, SYNE2, PSMD3, FLJ27255,         PRKCD, RAB34, RPN2//EEF1A2, SLC35B1, KCNIP3, PDE3B,         PXMP2//PGAM5, SDF2, HIST1H3I, LOC284757, TMEM33//DCAF4L1,         CSNK2A2, LSM10, PTTG11P, ADRB3//GOT1L1, PLXNA2, DIAPH2, BICD2,         HAL, RPS6KC1, TMEM106C, CD1E, SLC35A5, C7orf26, IMP3, PICALM,         ARF1, FHOD1//SLC9A5, C19orf55, TOMM40L//NR1I3, INSIG2, NEK9,         HCG27, SDHB, CUBN, PRDX3, CEPT1//DRAM2, ERGIC1, KPNA3, VAV1,         ELMO1, CUGBP2, LASP1, COL9A2, MEGF9, ELF4, SUZ12P,         SULT1A2//SULT1A1, FAM123C, FAR2, IER2, RGS2, MYBPH, MFAP3,         RCHY1, MGAT1, MFSD4, CDH2, TMEM184C, CTRB2//CTRB1, MPP4, PHF12,         SLBP, ADAM19, HTR1B, TRIM55, CRNN, KLHDC7A, YIPF5, SLC11A1,         GABBR1, CAMKV, SLC35F5, CHRNG, CXCL14, METTLE, PHC2, GPR153,         TNFRSF10D, BAT2L, GALNT2, DENND3//C8orf60, CLDN3, F11, CCDC93,         FLJ46365, CYP21A2, ETV5, TRPM2, IL20, NBL1//C1orf151, NGEF,         POU6F2, PTEN//PTENP1, NPC1L1, CYP4B1, NFIC, PPARGC1A, PLIN3,         THPO, TIMP4, CELSR2, DMBX1, CAMK2B, PPFIA1, HCLS1, SLC6A20,         C17orf66//RSL24D1, PIWIL2, DAZL//DAZ4//DAZ3//DAZ2, GAL3ST2,         TPD52L1, C19orf34, RASGEF1C, BAALC//FLJ10489, NR4A2//FLJ46875,         HAPLN1, CLDN18, TAS1R1, TIMD4, SKI, CCDC48, MEGF10, OSBPL6,         DNAH2, ARID1B, PGC, DCST1, SDK1, CHIA, REG4//NBPF7,         TMEM49//CLTC//MIR21, TMEM44, NDUFB6//DFFB, COL25A1, EPHX4,         NCRNA00085, NTRK3, PKHD1, SLC2A7, NTRK1, ABHD1//PREB, SLC4A9,         GPNMB, SLC5A1, SLC7A8, RTCD1, PROC, C17orf64, FLJ14100//C1orf86,         NUP50, UNKL, C10orf18, TMEM61, C9orf68, CYTSA, MORN3, RAB17,         CNNM3, CCDC28B, SH2D6, BARHL2, T, SNRK, TCP11, KDR, ENAM, UNKL,         SPRR2C, GPR17//LOC100291428//LIMS2, C1orf175//TTC4, CACNA1D,         C2orf62, LOC100132686, UNQ6126, TRIM15, GPR113//SELI, IL22,         SCN10A, FAAH, MBOAT7, C7orf51, KIAA1530, TRPM8, C1orf95,         DDC//LOC100129427, GABRA1, HCRTR1, DCST2, CHODL,         PANS//EEF1A1//CHCHD2, LYPD6B, UGT3A1 and SERPINA6 (hereafter         referred to interchangeably herein as “group F IRS immune system         biomarker genes” or “group F IRS biomarker genes”).

In some embodiments, the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first, second, third and fourth IRS immune system biomarker groups, wherein:

-   -   a) the first IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: RPS19BP1,         RFXANK//MEF2B//LOC729991, C19orf56, RTN2, HIST1H2BM, CETP,         PQLC1, H2AFX, KIAA0101//CSNK1G1, LOC100134934//CDK3, LPCAT4,         LPP, MPZL2, ANKRD9, RBM33, MYBL2, PLA2G7, OIP5, CRIPT, RNF186,         C3orf35, ZHX2, NDUFB7 and DUSP11 (hereafter referred to         interchangeably herein as “group G IRS immune system biomarker         genes” or “group G IRS biomarker genes”);     -   b) the second IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: PIWIL4, C11orf82,         ACRC, PLACE, ZDHHC4, OTX1, INSIG1, BATF, MFSD11, C15orf24, CDS2,         POLR2G, SLC39A9, FAM96B, TM7SF3, SPPL2A, ADIPOR2, GOSR2, DERL2,         TPST2, VWA5A//OR10D1P, CCDC101//LOC388242, MAPK6, PSMA7, JKAMP,         TLR10, RAG1AP1, SLC30A1, PDGFC, ATOX1, TMEM205//hCG_29977,         FAM108B1, UBQLN2, EDF1, C20orf197, RER1, UBE2E1, ANKRD10,         SEC22C, TM2D3, SLC15A2, CCDC109A, HIST1H4I, TSPAN31, TGFBR1,         CNIH, DDAH2, NIPA2//CYFIP1, BID, CYB5R1, KIT, RPGRIP1, MRPL41,         RUNX2, ITFG1, ZNRD1//NCRNA00171, NLRP1//LOC728392,         KIAA1257//ACAD9//LOC100132731, KLHL6, GALK2, DAD1//OR6J1,         PDLIM5, TRAM1/LOC286190, TNPO1, ACSL5, SYNE2, RPN2//EEF1A2,         SLC35B1, KCNIP3, TMEM33//DCAF4L1, CSNK2A2, LSM10, PLXNA2,         DIAPH2, HAL, RPS6KC1, SLC35A5, PICALM, C19orf55, INSIG2, SDHB,         PRDX3, CEPT1/DRAM2, KPNA3, SULT1A2//SULT1A1, FAR2, MYBPH, MFAP3,         RCHY1, CDH2, TMEM184C, CTRB2//CTRB1, SLBP, CRNN, YIPF5, CHRNG,         SLC35F5, METTLE, CLDN3, CCDC93, CYP21A2, NBL1//C1orf151, NGEF,         POU6F2, NPC1L1, PPARGC1A, THPO, CELSR2, DMBX1, SLC6A20,         C17orf66//RSL24D1, GAL3ST2, C19orf34, BAALC//FLJ10489, CLDN18,         TAS1R1, CCDC48, OSBPL6, SDK1, TMEM49//CLTC//MIR21, TMEM44,         NDUFB6//DFFB, NCRNA00085, NTRK3, NTRK1, SLC4A9, SLC5A1, RTCD1,         FLJ14100//C1orf86, PROC, C17orf64, UNKL, C9orf68, MORN3, RAB17,         CNNM3, CCDC28B, BARHL2, UNKL, LOC100132686, UNQ6126, IL22, FAAH,         C7orf51, DCST2, LYPD6B and SERPINA6 (hereafter referred to         interchangeably herein as “group H IRS immune system biomarker         genes” or “group H IRS biomarker genes”);     -   c) the third IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: ALKBH5//FLJ13773,         RNA243, ARD1A, CDC6, AHCY, MRPS34, CYBASC3, HIST1H4L, RNF114,         TRIM28, CSTF1, CEACAM4, GAB2, GNB5//LOC100129973, THAP11,         SSR4//IDH3G, STT3A, NUTF2, MPZL3, TMEM147, RAB34, PDE3B,         PXMP2//PGAM5, HIST1H3I, LOC284757, TMEM106C, STK17B, IMP3,         HCG27, CUBN, ERGIC1, ELMO1, CUGBP2, COL9A2, MEGF9, SUZ12P,         FAM123C, RGS2, PRR13//PCBP2, PHF12, ADAM19, GATAD2B//PLIN2,         SLC11A1, PPTC7, PHC2, BAT2L, DENND3//C8orf60, FLJ46365, ETV5,         CCDC125, PTEN//PTENP1, TLE3, NFIC, TIMP4, PPFIA1, HCLS1, SAP130,         MXD1, NR4A2//FLJ46875, SKI, ARID1B, ENTPD1/C10orf131, POGZ,         DOK3, REG4//NBPF7, MCM7, SLC7A8, NUP50, C10orf18, TMEM61, SH2D6,         SNRK, SPRR2C, CACNA1D, TRIM15, CLPS, MBOAT7, KIAA1530, C1orf95,         GABRA1, HCRTR1, CHODL and PAN3//EEF1A1//CHCHD2 (hereafter         referred to interchangeably herein as “group I IRS immune system         biomarker genes” or “group I IRS biomarker genes”); and,     -   d) the fourth IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: CLPTM1L, MAGED2,         DNASE1L1//RPL10, KEAP1, CUGBP1, CTSZ, CD63, ST3GAL2, SEC13,         TM9SF1, IRAK2, GABRR2, TG, CD300A, NEU1, ACSS2, AGTRAP, UPF0627,         COX15, ABCC2, ACTR1A, AIP, YIF1B, VPS26B, JAG1, VAT1, LAMP1,         DNASE1, MAZ, TAX1BP3, MGAT4B//SQSTM1, FBXW5, PTPN6, LZTR1, PLK1,         C22orf37, PSMD3, FLJ27255, PRKCD, SDF2, PTTG11P, ADRB3//GOT1L1,         BICD2, CD1E, C7orf26, ARF1, FHOD1//SLC9A5, TOMM40L//NR1I3, NEK9,         VAV1, SMARCD3, LASP1, ELF4, IER2, MGAT1, MFSD4, MDS2, MPP4,         HTR1B, TRIM55, KLHDC7A, GABBR1, CAMKV, CXCL14, GPR153,         TNFRSF10D, GALNT2, F11, IL20, TRPM2, CYP4B1, PLIN3, CAMK2B,         PIWIL2, DAZL//DAZ4//DAZ3//DAZ2, TPD52L1, RASGEF1C, HAPLN1,         TIMD4, CDK5RAP3, MEGF10, DNAH2, PGC, DCST1, CHIA, COL25A1,         EPHX4, PKHD1, SLC2A7, ABHD1//PREB, GPNMB, CYTSA, T, TCP11, ENAM,         KDR, GPR17//LOC100291428//LIMS2, C1orf175//TTC4, IL17F, C2orf62,         GPR113//SELI, SCN10A, TRPM8, DDC//LOC100129427 and UGT3A1         (hereafter referred to interchangeably herein as “group J IRS         immune system biomarker genes” or “group J IRS biomarker         genes”).

In specific embodiments, the first IRS immune system biomarker is a PLA2G7 expression product, the second IRS immune system biomarker is a PLAC8 expression product, the third IRS immune system biomarker is a CEACAM4 expression product and the fourth IRS immune system biomarker is a LAMP1 expression product.

In some embodiments, the indicator is for determining a likelihood of the subject having mild sepsis or severe sepsis, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

-   -   a) the first IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: N4BP2L2//CG030,         FAM96A, MINPP1, MORC3//DOPEY2, LSM8, PLEKHA3, MITD1, ATF4, B2M,         TMX3, ZNF273, PLEKHF2, UNQ2999, DPM1, OCLM, NADK, GPR65, SFRS3,         ZNF28, PFDN4, COQ10B, SLC30A6//DDX50, KPNA5, ATP6V1G1, HAUS2,         hCG_2039148, RAB33B, BET1, UBE2V2, ATP6AP2, SUBH/TMEM183A,         TMEM188, ABHD3, LAPTM4A, RNF138, CCDC82, TMEM179B, PAPD4, VAMP2,         CCDC126, ATG3, CHCHD5, RBM39//LOC643167, NAT8B, HAT1, CNOT6L,         ZBED5, GOLT1B, TTC33, ACTN1, ACTR6, GNB4, TMEM208, CCNH,         C4orf34, HAUS6//SCARNA8, TCTN1, SF3B14, TMEM138, TTC35,         DLEU2//DLEU2L, MFSD8, COX7A2L, UGGT2, CEPT1/DRAM2, LEMD3,         CREBZF, RPL21P44, ANGEL2, UFM1, XPO1, CALM2//C2orf61, PLDN,         CLK1//PPIL3, C1orf84, PPA2, FPGT//TNNI3K, ORC4L, SLC25A14,         H3F3B//H3F3C, FAM188A, MCM9, KLHL9, NACA, PAFAH1B2, CRLS1,         TSSK4, LOC152217, ZNF568, ATP6V0D1//LOC100132855, CDC37L1, FNTA,         SHFM1, JKAMP, TMEM126B, MRPL47, DENND1B, ATP6V0E1//SNORA74B,         LIN7C, HAUS3//POLN, SLC30A7, VAMP3, OBFC2A, MAGT1, STARD3NL,         C5orf15, PSMD10, RERE, RNF139, SFT2D2, SKP1, RNPC3//AMY2B,         MYOM1, TIPRL, HPRT1, TRIM21, VRK2, CDKN1B, ANKRA2, RAP2B,         FAM127B//FAM127C//FAM127A, FAM126A, TMEM161B, UNQ1887, FANCF,         SELT, CYP20A1, RWDD1, ARPP19, SC5DL,         TICAM2//TMED7//TMED7-TICAM2, STAM, LEPROTL1, RNF44, DCP1B,         TNNT3, UCHL5, UPRT, SON, PIK3C3, SFRS1//FLJ44342,         FBXO22//FBXO22OS, SCFD1, C11orf31, TMTC3, CCDC132,         TMBIM4//LOC100133322, ATAD1, APH1A, MYNN, HADHB, PIGN, RNA243,         SLC38A9, C10orf84, CDKN1A, ATP7A, RPAP2, ZNF451,         GSK3B//LOC100129275, TMSB10, KCTD5//PRO0461//PDPK1, VPS29,         WIPF3//ZNRF2//LOC441208, SFRS5 and C11orf73 (hereafter referred         to interchangeably herein as “group K IRS immune system         biomarker genes” or “group K IRS biomarker genes”); and,     -   b) the second IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: C13orf1, PRKCB,         APOBEC3A//APOBEC3B, SFRS9, NCAPD2//SCARNA10//FADS1, GATS,         LOC284757, TSHZ2, JAK1, MAPK13, RPN1, GNAS, CYTSA, TRPM6,         C22orf30, PCMTD1//PXDNL, CCDC69, ARSD, MLL3//BAGE2,         NCOR1/C20orf191//LOC100131704, MRPS7, VEZF1, GSR, POU2F1, VPS4A,         SMG7, PTP4A2, OSBP, GLCCI1/tcag7.903, DOCK2, PCNX, GLTP, FBXO18,         YY2, TCF20, NR2C2//MRPS25, TEX2, BAMBI, WHSC1L1, UBTF, FAH,         GEMIN4, DDEF11T1, FAM50A, VPS13D, SATB1//TBC1D5, PARP6, SETD5,         PHF21A, IRF4, ZNF217, UBE4A, HIVEP1, HDLBP, GNAI2, MED13L,         FOXP1, NSD1, DDOST, TMBIM6, ABLIM1, SYNRG//LOC100131822, KDM3B,         ASH1L, NCOA2, GPRIN3, NCOA1, KLF3, LOC100288114//MGC9913,         VPS26B, AHCYL1, CDC6, PLCG2, IL16, GIT2, TACC3, MAP4K4, NEK9,         FAM149B1//FAM149B2, VPS8, ATXN7, WDFY4, ZC3H11A//RP11-74E24.2,         THRAP3, ZNF346, AP3M2, CD14, CLASP1, ABCC2,         ATXN7L1//RINT1//EFCAB10, INO80D, CTPS,         LRRC37A3//LRRC37A2//LRRC37A//ARL17P1//LRRC37A4//LOC100294335//LO         C644397, DNASE1, LRRN3, ZNF318, PRKAR2A, MRPS15,         ANKHD1-EIF4EBP3//ANKHD1/EIF4EBP3, BTF3L4, DGKA, C10orf119, MBD5,         C11orf30, CDC2L5, DPP4, DCTN4, TP53BP2, IMPDH2, GOT2, ELMO1 and         PARP1 (hereafter referred to interchangeably herein as “group L         IRS immune system biomarker genes” or “group L IRS biomarker         genes”).

In some embodiments, the indicator is for determining a likelihood of the subject having mild sepsis or septic shock, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

-   -   a) the first IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: EEF1DP3, GIMAP7,         ZNF839, PYGL, TNFAIP8L2, SFRS9, VIM, GLTP, WDFY4, APPL2, C4orf3,         PLD1, LIN7A, ELP2, ZDHHC3, UBAP1/K1F24, C20orf177,         FAM149B1//FAM149B2, E2F3, SPATA1, DACH1, FAM47E//STBD1,         SVIL//hCG_1783494, METTL9, LRRC42, NUPL1, UPP1, AFF2, SLC16A4,         SET, CA4, HCK, C16orf72, EXT1, NOP58, FRZB, C9orf6//IKBKAP,         VASP, ASB8//PHB, GTDC1, SLC39A9, FBXO34//KIAA0831,         RABGEF1//tcag7.967//tcag7.951//KCTD7//LOC100293333, SLC28A3,         WIP11//ARSG, NFE2, GOLGA1/SCAI, C9orf84, RPS6KA2, PSMA7,         C19orf59, ICA1, TOR1AIP2//TOR1AIP1//IFRG15, MSRA, FPR1, TP53I3,         FOXL2, CD63, PIGC, CENPBD1, CYB5R1, GNB2, ZNF197, KLF7, NSFL1C,         USF2, PARP6, MAP9, TSPO, CSTB, DDA1, SLC36A4, GFOD2, OCRL,         ZNF232, APH1B, TALDO1/INTS8, DENND2C//BCAS2, RAB11FIP2, LARS,         PLP2, EIF4E2, DNASE1L1//RPL10, AFTPH, TMCO3, RPA2, UQCRC1,         ZDHHC3, and ACTR1A (hereafter referred to interchangeably herein         as “group M IRS immune system biomarker genes” or “group M IRS         biomarker genes”); and,     -   b) the second IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: CD6, ITPA,         PVRIG//PILRB//STAG3, FLT3LG, IL12RB1, MAP3K14//LOC100133991,         FAM102A, TMC8, TMEM208, TMEM109, C1orf84, NADK, SEPT1, UBA7,         CD5, C12orf62, C20orf112, FOXP4, EIF4A2//SNORA4,         ZNF487//LOC439911, KCTD13, IL18BP//NUMA1, KPNA5, EDC4,         ZNF587//ZNF417, NBR2, RPL28, ZNF738, SHFM1, CNO, C9orf82,         RPL5//SNORA66//SNORD21//FAM69A, VAMP2, SIT1, SFRS3, LCK, IRF9,         MRPS21, NEFM//LOC100129717, RCN2, BET1, C19orf6, SH2D1A, GLS,         OR1C1, RBM14//RBM4, CCDC97, TUT1, MRPL14, ENOPH1, NAGK, DPM1,         MPV17, SH2D3C, TMEM204, C3orf42, ARSD, FAM96A, LSM8, ATP5G1,         KTI12, ARL4C, C11orf31, C16orf58//LOC100128371,         CCDC109B//HIGD1A//CCDC13, NUDT21, ZFP106, ACTR6, LIX1L, MEF2D,         ZNF407, TMEM18, NAT11, DNAJC24, PLEKHA3, GPN2, SMCR7,         C7orf23//DMTF1, PPM1G, CNOT6L, NACA, FNTA, GRIA2,         N4BP2L2//CG030, ENOSF1/TYMS, THBS4, LUC7L2, MOCS2, ZNF383, AKNA,         UBE2Z, FLJ34077, SH3KBP1, POLR2F//LOC100131530, ILIA, UBE2V2,         KIAA1919, PRKCB, SHOC2, RBM46, GRPEL2, KCNG3, PCDH10, XAB2,         VPS52, MCCC2, NSMCE4A, PTP4A2, SNX2, COQ10A, C6orf182, RNF44,         MOGS, DIRAS3, Mitochondrial, KIAA1826, SGK196,         NSUN5//NSUN5B//NSUN5C, Mitochondrial, MORF4L2, MAK16//C8orf41,         PILRB//PVRIG//STAG3, SAAL1, TMX3, PTPRG, MAPK1, DNAJA3, LEAP2,         LMOD3, ASB6, MTMR10//MTMR15, HIBADH, MORC1, CORO1A//LOC606724,         SFXN2, HSN2, AAAS, INHBA, MRPS7, LRRFIP1, KCTD7//RABGEF1,         DCDC2//KAAG1, SLCO3A1, DENND4B, CFTR, MOG, QRFPR, BAT2//SNORA38,         ITPR3, C3orf22, TNFRSF4, ZNF646//ZNF668, GCM2, CDK4//TSPAN31,         FXC1, RNMT, CHST4, POLR3E, PDE8B, C6orf146, CHCHD5, TARBP1,         TADA1L, PREX2, TRAP1//DNASE1, ZNHIT6, RGP1//GBA2, SMCP, ZC3H15,         TRAPPC4, SARNP//DNAJC14, GNAS, C14orf104, IL20RA, WAC, SLIT3,         C4orf39, TSEN54, PPP1R13B, TRIM35, HGC6.3, YIPF3,         HQ0644/PRO0644, FAM13C//PHYHIPL, BCCIP//DHX32, ACADM,         SUB1//TMEM183A, KPNA1, SPAG17//WDR3, KIAA1549, DIABLO//B3GNT4,         ZBTB44, EIF1AD, RUNX1T1, TRIL, PTPLB, DHDDS, UPK1A, OTUB1,         C1orf182, HAPLN2, SOBP, RYR3, LRRC17, TKTL2, TMBIM6, and GDNF         (hereafter referred to interchangeably herein as “group N IRS         immune system biomarker genes” or “group N IRS biomarker         genes”).

In some embodiments, the indicator is for determining a likelihood of the subject having severe sepsis or septic shock, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

-   -   a) the first IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: SIRPG//SIRPA,         GATA3, FAM102A, UPF3A, ATP13A5, CACNA1I and RANBP17//USP12         (hereafter referred to interchangeably herein as “group O IRS         immune system biomarker genes” or “group O IRS biomarker         genes”); and,     -   b) the second IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from the         following IRS immune system biomarker genes: GABRA6, HAPLN1,         YSK4, FOXL2, TLL1, MECOM, COL3A1, HRG, SLC22A3, C8orf45, SCN7A         and SNTG1 (hereafter referred to interchangeably herein as         “group P IRS immune system biomarker genes” or “group P IRS         biomarker genes”).

In another broad form the present invention seeks to provide apparatus for determining an indicator used in assessing a likelihood of a biological subject having a presence, absence, degree or prognosis of at least one medical condition, the apparatus including at least one electronic processing device that:

-   -   a) determines a pair of biomarker values, each biomarker value         being a value measured or derived for at least one corresponding         immune system biomarker of the biological subject and being at         least partially indicative of a concentration of the immune         system biomarker in a sample taken from the subject;     -   b) determines a derived biomarker value using the pair of         biomarker values, the derived biomarker value being indicative         of a ratio of concentrations of the pair of immune system         biomarkers; and,     -   c) determines the indicator using the derived biomarker value.

In another broad form the present invention seeks to provide a composition comprising at least one pair of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least one pair of reverse transcribed mRNAs comprising a first pair and a second pair of reverse transcribed mRNAs, wherein the first pair comprises a PLAC8 reverse transcribed mRNA and a PLA2G7 reverse transcribed mRNA and wherein the second pair comprises a CEACAM4 reverse transcribed mRNA and a LAMP1 reverse transcribed mRNA.

In another broad form the present invention seeks to provide a composition comprising at least one pair of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least one pair of reverse transcribed mRNAs comprising a reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group A IRS immune system biomarker genes and a reverse transcribed mRNA from a second IRS immune system biomarker gene selected from group B IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a composition comprising at least one pair of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least one pair of reverse transcribed mRNAs comprising a reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group C IRS immune system biomarker genes and a reverse transcribed mRNA from a second IRS immune system biomarker gene selected from group D IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a composition comprising at least one pair of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least one pair of reverse transcribed mRNAs comprising a reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group E IRS immune system biomarker genes and a reverse transcribed mRNA from a second IRS immune system biomarker gene selected from group F IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a composition comprising at least two pairs of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least two pairs of reverse transcribed mRNAs comprising a first pair and a second pair of reverse transcribed mRNAs, wherein the first pair comprises a reverse transcribed mRNA from a first IRS immune system biomarker gene and a reverse transcribed mRNA from a second IRS immune system biomarker gene, and wherein the second pair comprises a reverse transcribed mRNA from a third IRS immune system biomarker gene and a reverse transcribed mRNA from a fourth IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group G IRS immune system biomarker genes, wherein the second IRS immune system biomarker gene is selected from group H IRS immune system biomarker genes, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes, and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a composition comprising at least one pair of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least one pair of reverse transcribed mRNAs comprising a reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group K IRS immune system biomarker genes and a reverse transcribed mRNA from a second IRS immune system biomarker gene selected from group L IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a composition comprising at least one pair of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least one pair of reverse transcribed mRNAs comprising a reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group M IRS immune system biomarker genes and a reverse transcribed mRNA from a second IRS immune system biomarker gene selected from group N IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a composition comprising at least one pair of reverse transcribed mRNAs and at least one oligonucleotide primer or probe that hybridizes to an individual one of the reverse transcribed mRNAs, the at least one pair of reverse transcribed mRNAs comprising a reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group O IRS immune system biomarker genes and a reverse transcribed mRNA from a second IRS immune system biomarker gene selected from group P IRS immune system biomarker genes.

The at least one oligonucleotide primer or probe can be hybridized to an individual one of the reverse transcribed mRNAs.

The reverse transcribed mRNAs can be derived from components of the immune system.

The reverse transcribed mRNAs can be derived from leukocytes.

The reverse transcribed mRNAs can be derived from blood cells.

The reverse transcribed mRNAs can be derived from peripheral blood cells.

The composition can further comprise a labeled reagent for detecting the reverse transcribed mRNAs.

The labeled reagent can be a labeled said at least one oligonucleotide primer or probe.

The labeled reagent can be a labeled said reverse transcribed mRNA.

The labeled reagent can be a labeled oligonucleotide linker or tag for labeling a said reverse transcribed mRNA.

In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and ipSIRS, the kit comprising at least one pair of reagents comprising a first pair of reagents and a second pair of reagents, wherein the first pair of reagents comprises (i) a reagent that allows quantification of a polynucleotide expression product of the PLA2G7 gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of the PLAC8 gene, wherein the second pair of reagents comprises: (iii) a reagent that allows quantification of a polynucleotide expression product of the CEACAM4 gene; and (iv) a reagent that allows quantification of a polynucleotide expression product of the LAMP1 gene.

In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and a healthy condition, the kit comprising at least one pair of reagents comprising (i) a reagent that allows quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group A IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group B IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of ipSIRS and a healthy condition, the kit comprising at least one pair of reagents comprising (i) a reagent that allows quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group C IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group D IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and ipSIRS, the kit comprising at least one pair of reagents comprising (i) a reagent that allows quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group E IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group F IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and ipSIRS, the kit comprising at least two pairs of reagents comprising a first pair of reagents and a second pair of reagents, wherein the first pair of reagents comprises (i) a reagent that allows quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, and wherein the second pair of reagents comprises (i) a reagent that allows quantification of a polynucleotide expression product of a third IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a fourth IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group G IRS immune system biomarker genes, wherein the second IRS immune system biomarker gene is selected from group H IRS immune system biomarker genes, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes, and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of mild sepsis and severe sepsis, the kit comprising at least one pair of reagents comprising (i) a reagent that allows quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group K IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group L IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of mild sepsis and septic shock, the kit comprising at least one pair of reagents comprising (i) a reagent that allows quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group M IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group N IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of severe sepsis and septic shock, the kit comprising at least one pair of reagents comprising (i) a reagent that allows quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) a reagent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group O IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group P IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a method for inhibiting the development or progression in a subject of at least one condition selected from the group consisting of inSIRS and ipSIRS, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of the at least one condition in the subject, the indicator-determining method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the pair of biomarker values comprises at least one of:

-   -   a) a first pair of biomarker values comprising first and second         biomarker values corresponding to first and second biomarkers,         wherein the first immune system biomarker represents a         polynucleotide expression product of the PLA2G7 gene and wherein         the second immune system biomarker representing a polynucleotide         expression product of the PLAC8 gene, and     -   b) a second pair of biomarker values comprises third and fourth         biomarker values corresponding to third and fourth immune system         biomarkers, respectively, wherein the third immune system         biomarker represents a polynucleotide expression product of the         CEACAM4 gene and wherein the fourth immune system biomarker         represents a polynucleotide expression product of the LAMP1         gene.

Typically the indicator-determining method comprises: determining the first pair and second pair of biomarker values and determining a first derived biomarker value calculated using the first pair of biomarker values and a second derived biomarker value calculated using the second pair of biomarker values; and determining the indicator based on a combination of the first and second derived biomarker values.

In another broad form the present invention seeks to provide a method for inhibiting the development or progression of inSIRS in a subject, the method comprising: exposing the subject to a treatment regimen for treating inSIRS based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of inSIRS in the subject, the indicator-determining method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises first and second biomarker values corresponding to first and second immune system biomarkers, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group A IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group B IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a method for inhibiting the development or progression of ipSIRS in a subject, the method comprising: exposing the subject to a treatment regimen for treating ipSIRS based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of ipSIRS in the subject, the indicator-determining method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises first and second biomarker values corresponding to first and second immune system biomarkers, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group C IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group D IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a method for inhibiting the development or progression in a subject of at least one condition selected from the group consisting of inSIRS and ipSIRS, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of the at least one condition in the subject, the indicator-determining method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises first and second biomarker values corresponding to of first and second immune system biomarkers, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group E IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group F IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a method for inhibiting the development or progression in a subject of at least one condition selected from the group consisting of inSIRS and ipSIRS, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of the at least one condition in the subject, the indicator-determining method comprising: (a) determining at least two pairs of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least two derived biomarker values using the at least two pairs of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of each pair of immune system biomarkers; and (c) determining the indicator based on the at least two derived biomarker values, wherein the at least one pair of biomarker values comprises a first pair of biomarker values comprising first and second biomarker values corresponding to first and second immune system biomarkers, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, and a second pair of biomarker values comprising third and fourth biomarker values corresponding to third and fourth immune system biomarkers, respectively, wherein the third immune system biomarker represents a polynucleotide expression product of a third IRS immune system biomarker gene and wherein the fourth immune system biomarker represents a polynucleotide expression product of a fourth IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group G IRS immune system biomarker genes, wherein the second IRS immune system biomarker gene is selected from group H IRS immune system biomarker genes, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes, and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a method for inhibiting the development or progression in a subject of at least one condition selected from the group consisting of mild sepsis and severe sepsis, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of the at least one condition in the subject, the indicator-determining method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises first and second biomarker values corresponding to first and second immune system biomarkers, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group K IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group L IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a method for inhibiting the development or progression in a subject of at least one condition selected from the group consisting of mild sepsis and septic shock, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of the at least one condition in the subject, the indicator-determining method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises first and second biomarker values corresponding to first and second immune system biomarkers, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group M IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group N IRS immune system biomarker genes.

In another broad form the present invention seeks to provide a method for inhibiting the development or progression in a subject of at least one condition selected from the group consisting of severe sepsis and septic shock, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator-determining method, wherein the indicator is indicative of the presence of the at least one condition in the subject, the indicator-determining method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises first and second biomarker values corresponding to first and second immune system biomarkers, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group O IRS immune system biomarker genes and wherein the second IRS immune system biomarker gene is selected from group P IRS immune system biomarker genes.

In some embodiments, the method comprises taking the sample from the subject and obtaining the indicator according to the indicator-determining method.

In some embodiments, the method comprises: sending the sample taken from the subject to a laboratory at which the indicator is determined.

Typically, the sample comprises cells obtained from the subject or a nucleic acid sample thereof.

In another broad form the present invention seeks to provide a method for differentiating between inSIRS and ipSIRS in a biological subject, the method including:

-   -   a) obtaining a sample taken from a biological subject showing a         clinical sign of SIRS, the sample including polynucleotide         expression products;     -   b) quantifying polynucleotide expression products within the         sample to determine a pair of biomarker values, the pair of         biomarker values being selected from the group consisting of:         -   i) a first pair of biomarker values indicative of a             concentration of polynucleotide expression products of the             PLA2G7 gene and PLAC8 gene;         -   ii) a second pair of biomarker values indicative of a             concentration of polynucleotide expression products of the             CEACAM4 gene and LAMP1 gene;     -   c) determining an indicator indicative of a ratio of         concentrations of the polynucleotide expression products using         the pair of biomarker values; and,     -   d) comparing the indicator to first and second indicator         references, the first and second indicator references being         indicative of inSIRS and ipSIRS, respectively; and,     -   e) determining a likelihood of the subject having inSIRS or         ipSIRS in accordance with the results of the comparison.

In another broad form the present invention seeks to provide a method for differentiating between inSIRS and a healthy condition in a biological subject, the method including:

-   -   a) obtaining a sample taken from a biological subject showing a         clinical sign of SIRS, the sample including polynucleotide         expression products;     -   b) quantifying polynucleotide expression products within the         sample to determine a pair of biomarker values, the pair of         biomarker values being indicative of a concentration of         polynucleotide expression products of a first IRS immune system         biomarker gene and a second IRS immune system biomarker gene,         wherein the first IRS immune system biomarker gene is selected         from group A IRS immune system biomarker genes and wherein the         second IRS immune system biomarker gene is selected from group B         IRS immune system biomarker genes;     -   c) determining an indicator indicative of a ratio of         concentrations of the polynucleotide expression products using         the pair of biomarker values; and,     -   d) comparing the indicator to first and second indicator         references, the first and second indicator references being         indicative of inSIRS and healthy condition, respectively; and,     -   e) determining a likelihood of the subject having inSIRS or the         healthy condition in accordance with the results of the         comparison.

In another broad form the present invention seeks to provide a method for differentiating between ipSIRS and a healthy condition in a biological subject, the method including:

-   -   a) obtaining a sample taken from a biological subject showing a         clinical sign of SIRS, the sample including polynucleotide         expression products;     -   b) quantifying polynucleotide expression products within the         sample to determine a pair of biomarker values, the pair of         biomarker values being indicative of a concentration of         polynucleotide expression products of a first IRS immune system         biomarker gene and a second IRS immune system biomarker gene,         wherein the first IRS immune system biomarker gene is selected         from group C IRS immune system biomarker genes and wherein the         second IRS immune system biomarker gene is selected from group D         IRS immune system biomarker genes;     -   c) determining an indicator indicative of a ratio of         concentrations of the polynucleotide expression products using         the pair of biomarker values; and,     -   d) comparing the indicator to first and second indicator         references, the first and second indicator references being         indicative of ipSIRS and healthy condition, respectively; and,     -   e) determining a likelihood of the subject having ipSIRS or the         healthy condition in accordance with the results of the         comparison.

In another broad form the present invention seeks to provide a method for differentiating between inSIRS and ipSIRS in a biological subject, the method including:

-   -   a) obtaining a sample taken from a biological subject showing a         clinical sign of SIRS, the sample including polynucleotide         expression products;     -   b) quantifying polynucleotide expression products within the         sample to determine a pair of biomarker values, the pair of         biomarker values being indicative of a concentration of         polynucleotide expression products of a first IRS immune system         biomarker gene and a second IRS immune system biomarker gene,         wherein the first IRS immune system biomarker gene is selected         from group E IRS immune system biomarker genes and wherein the         second IRS immune system biomarker gene is selected from group F         IRS immune system biomarker genes;     -   c) determining an indicator indicative of a ratio of         concentrations of the polynucleotide expression products using         the pair of biomarker values; and,     -   d) comparing the indicator to first and second indicator         references, the first and second indicator references being         indicative of inSIRS and ipSIRS, respectively; and,     -   e) determining a likelihood of the subject having inSIRS or         ipSIRS in accordance with the results of the comparison.

In another broad form the present invention seeks to provide a method for differentiating between inSIRS and ipSIRS in a biological subject, the method including:

-   -   a) obtaining a sample taken from a biological subject showing a         clinical sign of SIRS, the sample including polynucleotide         expression products;     -   b) quantifying polynucleotide expression products within the         sample to determine a pair of biomarker values, the pair of         biomarker values being selected from the group consisting of:         -   i) a first pair of biomarker values indicative of a             concentration of polynucleotide expression products of a             first IRS immune system biomarker gene and a second IRS             immune system biomarker gene, wherein the first IRS immune             system biomarker gene is selected from group G IRS immune             system biomarker genes and wherein the second IRS immune             system biomarker gene is selected from group H IRS immune             system biomarker genes;         -   ii) a second pair of biomarker values indicative of a             concentration of polynucleotide expression products of a             third IRS immune system biomarker gene and a fourth IRS             immune system biomarker gene, wherein the third IRS immune             system biomarker gene is selected from group I IRS immune             system biomarker genes and wherein the fourth IRS immune             system biomarker gene is selected from group J IRS immune             system biomarker genes;     -   c) determining an indicator indicative of a ratio of         concentrations of the polynucleotide expression products using         the pair of biomarker values; and,     -   d) comparing the indicator to first and second indicator         references, the first and second indicator references being         indicative of inSIRS and ipSIRS, respectively; and,     -   e) determining a likelihood of the subject having inSIRS or         ipSIRS in accordance with the results of the comparison.

In another broad form the present invention seeks to provide a method for differentiating between mild sepsis and severe sepsis in a biological subject, the method including:

-   -   a) obtaining a sample taken from a biological subject showing a         clinical sign of SIRS, the sample including polynucleotide         expression products;     -   b) quantifying polynucleotide expression products within the         sample to determine a pair of biomarker values, the pair of         biomarker values being indicative of a concentration of         polynucleotide expression products of a first IRS immune system         biomarker gene and a second IRS immune system biomarker gene,         wherein the first IRS immune system biomarker gene is selected         from group K IRS immune system biomarker genes and wherein the         second IRS immune system biomarker gene is selected from group L         IRS immune system biomarker genes;     -   c) determining an indicator indicative of a ratio of         concentrations of the polynucleotide expression products using         the pair of biomarker values; and,     -   d) comparing the indicator to first and second indicator         references, the first and second indicator references being         indicative of mild sepsis and severe sepsis, respectively; and,     -   e) determining a likelihood of the subject having mild sepsis or         severe sepsis in accordance with the results of the comparison.

In another broad form the present invention seeks to provide a method for differentiating between mild sepsis and septic shock in a biological subject, the method including:

-   -   a) obtaining a sample taken from a biological subject showing a         clinical sign of SIRS, the sample including polynucleotide         expression products;     -   b) quantifying polynucleotide expression products within the         sample to determine a pair of biomarker values, the pair of         biomarker values being indicative of a concentration of         polynucleotide expression products of a first IRS immune system         biomarker gene and a second IRS immune system biomarker gene,         wherein the first IRS immune system biomarker gene is selected         from group M IRS immune system biomarker genes and wherein the         second IRS immune system biomarker gene is selected from group N         IRS immune system biomarker genes;     -   c) determining an indicator indicative of a ratio of         concentrations of the polynucleotide expression products using         the pair of biomarker values; and,     -   d) comparing the indicator to first and second indicator         references, the first and second indicator references being         indicative of mild sepsis and septic shock, respectively; and,     -   e) determining a likelihood of the subject having mild sepsis or         septic shock in accordance with the results of the comparison.

In another broad form the present invention seeks to provide a method for differentiating between severe sepsis and septic shock in a biological subject, the method including:

-   -   a) obtaining a sample taken from a biological subject showing a         clinical sign of SIRS, the sample including polynucleotide         expression products;     -   b) quantifying polynucleotide expression products within the         sample to determine a pair of biomarker values, the pair of         biomarker values being indicative of a concentration of         polynucleotide expression products of a first IRS immune system         biomarker gene and a second IRS immune system biomarker gene,         wherein the first IRS immune system biomarker gene is selected         from group O IRS immune system biomarker genes and wherein the         second IRS immune system biomarker gene is selected from group P         IRS immune system biomarker genes;     -   c) determining an indicator indicative of a ratio of         concentrations of the polynucleotide expression products using         the pair of biomarker values; and,     -   d) comparing the indicator to first and second indicator         references, the first and second indicator references being         indicative of severe sepsis and septic shock, respectively; and,     -   e) determining a likelihood of the subject having severe sepsis         or septic shock in accordance with the results of the         comparison.

Typically the method includes determining:

-   -   a) a first derived biomarker value indicative of a ratio of         concentrations of the polynucleotide expression products using         the first pair of biomarker values;     -   b) a second derived biomarker value indicative of a ratio of         concentrations of the polynucleotide expression products using         the first pair of biomarker values; and,     -   c) determining the indicator by combining the first and second         derived biomarker values.

Typically the first and second indicator references are distributions of indicators determined for first and second groups of a reference population, the first and second group consisting of individuals diagnosed with inSIRS and ipSIRS respectively.

In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:

-   -   a) obtaining a sample taken from a biological subject, the         sample including polynucleotide expression products;     -   b) amplifying at least some polynucleotide expression products         in the sample;     -   c) determining an amplification amount representing a degree of         amplification required to obtain a defined level of each of a         pair of polynucleotide expression products selected from the         group consisting of:         -   i) a first pair of polynucleotide expression products of the             PLA2G7 gene and PLAC8 gene;         -   ii) a second pair of polynucleotide expression products of             the CEACAM4 gene and LAMP1 gene;     -   d) determining the indicator by determining a difference between         the amplification amounts; and,     -   e) using the indicator to assess the likelihood of a biological         subject having a medical condition.

In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:

-   -   a) obtaining a sample taken from a biological subject, the         sample including polynucleotide expression products;     -   b) amplifying at least some polynucleotide expression products         in the sample;     -   c) determining an amplification amount representing a degree of         amplification required to obtain a defined level of each of a         pair of polynucleotide expression products selected from the         group consisting of: a polynucleotide expression product of a         first IRS immune system biomarker gene and a polynucleotide         expression product of a second IRS immune system biomarker gene,         wherein the first IRS immune system biomarker gene is selected         from group A IRS immune system biomarker genes and wherein the         second IRS immune system biomarker gene is selected from group B         IRS immune system biomarker genes;     -   d) determining the indicator by determining a difference between         the amplification amounts; and,     -   e) using the indicator to assess the likelihood of a biological         subject having a medical condition.

In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:

-   -   a) obtaining a sample taken from a biological subject, the         sample including polynucleotide expression products;     -   b) amplifying at least some polynucleotide expression products         in the sample;     -   c) determining an amplification amount representing a degree of         amplification required to obtain a defined level of each of a         pair of polynucleotide expression products selected from the         group consisting of: a polynucleotide expression product of a         first IRS immune system biomarker gene and a polynucleotide         expression product of a second IRS immune system biomarker gene,         wherein the first IRS immune system biomarker gene is selected         from group C IRS immune system biomarker genes and wherein the         second IRS immune system biomarker gene is selected from group D         IRS immune system biomarker genes;     -   d) determining the indicator by determining a difference between         the amplification amounts; and,     -   e) using the indicator to assess the likelihood of a biological         subject having a medical condition.

In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:

-   -   a) obtaining a sample taken from a biological subject, the         sample including polynucleotide expression products;     -   b) amplifying at least some polynucleotide expression products         in the sample;     -   c) determining an amplification amount representing a degree of         amplification required to obtain a defined level of each of a         pair of polynucleotide expression products selected from the         group consisting of: a polynucleotide expression product of a         first IRS immune system biomarker gene and a polynucleotide         expression product of a second IRS immune system biomarker gene,         wherein the first IRS immune system biomarker gene is selected         from group E IRS immune system biomarker genes and wherein the         second IRS immune system biomarker gene is selected from group F         IRS immune system biomarker genes;     -   d) determining the indicator by determining a difference between         the amplification amounts; and,     -   e) using the indicator to assess the likelihood of a biological         subject having a medical condition.

In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:

-   -   a) obtaining a sample taken from a biological subject, the         sample including polynucleotide expression products;     -   b) amplifying at least some polynucleotide expression products         in the sample;     -   c) determining an amplification amount representing a degree of         amplification required to obtain a defined level of each of a         pair of polynucleotide expression products selected from the         group consisting of:         -   i) a first pair of polynucleotide expression products of a             first IRS immune system biomarker gene and a second IRS             immune system biomarker gene, wherein the first IRS immune             system biomarker gene is selected from group G IRS immune             system biomarker genes and wherein the second IRS immune             system biomarker gene is selected from group H IRS immune             system biomarker genes;         -   ii) a second pair of polynucleotide expression products of a             third IRS immune system biomarker gene and a fourth IRS             immune system biomarker gene, wherein the third IRS immune             system biomarker gene is selected from group I IRS immune             system biomarker genes and wherein the fourth IRS immune             system biomarker gene is selected from group J IRS immune             system biomarker genes;     -   d) determining the indicator by determining a difference between         the amplification amounts; and,     -   e) using the indicator to assess the likelihood of a biological         subject having a medical condition.

In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:

-   -   a) obtaining a sample taken from a biological subject, the         sample including polynucleotide expression products;     -   b) amplifying at least some polynucleotide expression products         in the sample;     -   c) determining an amplification amount representing a degree of         amplification required to obtain a defined level of each of a         pair of polynucleotide expression products selected from the         group consisting of: a polynucleotide expression product of a         first IRS immune system biomarker gene and a polynucleotide         expression product of a second IRS immune system biomarker gene,         wherein the first IRS immune system biomarker gene is selected         from group K IRS immune system biomarker genes and wherein the         second IRS immune system biomarker gene is selected from group L         IRS immune system biomarker genes;     -   d) determining the indicator by determining a difference between         the amplification amounts; and,     -   e) using the indicator to assess the likelihood of a biological         subject having a medical condition.

In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:

-   -   a) obtaining a sample taken from a biological subject, the         sample including polynucleotide expression products;     -   b) amplifying at least some polynucleotide expression products         in the sample;     -   c) determining an amplification amount representing a degree of         amplification required to obtain a defined level of each of a         pair of polynucleotide expression products selected from the         group consisting of: a polynucleotide expression product of a         first IRS immune system biomarker gene and a polynucleotide         expression product of a second IRS immune system biomarker gene,         wherein the second IRS immune system biomarker gene is selected         from group M IRS immune system biomarker genes and wherein the         second IRS immune system biomarker gene is selected from group N         IRS immune system biomarker genes;     -   d) determining the indicator by determining a difference between         the amplification amounts; and,     -   e) using the indicator to assess the likelihood of a biological         subject having a medical condition.

In another broad form the present invention seeks to provide a method for determining an indicator used in assessing the likelihood of a biological subject having at least one medical condition, the method including:

-   -   a) obtaining a sample taken from a biological subject, the         sample including polynucleotide expression products;     -   b) amplifying at least some polynucleotide expression products         in the sample;     -   c) determining an amplification amount representing a degree of         amplification required to obtain a defined level of each of a         pair of polynucleotide expression products selected from the         group consisting of: a polynucleotide expression product of a         first IRS immune system biomarker gene and a polynucleotide         expression product of a second IRS immune system biomarker gene,         wherein the first IRS immune system biomarker gene is selected         from group O IRS immune system biomarker genes and wherein the         second IRS immune system biomarker gene is selected from group P         IRS immune system biomarker genes;     -   d) determining the indicator by determining a difference between         the amplification amounts; and,     -   e) using the indicator to assess the likelihood of a biological         subject having a medical condition.

Typically the method includes determining:

-   -   a) a first derived biomarker value by determining a difference         between the amplification amounts of the first pair of         polynucleotide expression products;     -   b) a second derived biomarker value by determining a difference         between the amplification amounts of the second pair of         polynucleotide expression products;     -   c) determining the indicator by adding the first and second         derived biomarker values.

Typically the method includes determining:

-   -   a) comparing the indicator to first and second indicator         references, wherein the first and second indicator references         are distributions of indicators determined for first and second         groups of a reference population, one of the first and second         groups consisting of individuals diagnosed with the medical         condition; and,     -   b) determining a likelihood of the subject having the medical         condition in accordance with the results of the comparison.

Typically the amplification amount is at least one of:

-   -   a) a cycle time;     -   b) a number of cycles;     -   c) a cycle threshold;     -   d) an amplification time; and,     -   e) relative to an amplification amount of another amplified         product.

In another broad form the present invention seeks to provide a method for use in assessing the likelihood of a biological subject having a medical condition, the method including, in one or more processing devices:

-   -   a) determining a pair of biomarker values, the pair of biomarker         values being selected from the group consisting of:         -   i) a first pair of biomarker values indicative of a             concentration of polynucleotide expression products of the             PLA2G7 gene and PLAC8 gene;         -   ii) a second pair of biomarker values indicative of a             concentration of polynucleotide expression products of the             CEACAM4 gene and LAMP1 gene;     -   b) determining an indicator indicative of a ratio of the         concentrations of the polynucleotide expression products using         the pair of biomarker values;     -   c) retrieving previously determined first and second indicator         references from a database, the first and second indicator         references being determined based on indicators determined from         first and second groups of a reference population, one of the         groups consisting of individuals diagnosed with the medical         condition;     -   d) comparing the indicator to the first and second indicator         references;     -   e) using the results of the comparison to determine a         probability indicative of the subject having the medical         condition; and,     -   f) generating a representation of the probability, the         representation being displayed to a user to allow the user to         assess the likelihood of a biological subject having at least         one medical condition.

Typically the method includes determining:

-   -   a) a first derived biomarker value indicative of a ratio of         concentrations of the polynucleotide expression products using         the first pair of biomarker values;     -   b) a second derived biomarker value indicative of a ratio of         concentrations of the polynucleotide expression products using         the first pair of biomarker values; and,     -   c) determining the indicator by combining the first and second         derived biomarker values.

In another broad form the present invention seeks to provide apparatus for determining an indicator used in determining the likelihood of a biological subject having at least one medical condition, the apparatus including:

-   -   a) a sampling device that obtains a sample taken from a         biological subject, the sample including polynucleotide         expression products;     -   b) a measuring device that quantifies polynucleotide expression         products within the sample to determine a pair of biomarker         values, the pair of biomarker values being selected from the         group consisting of:         -   i) a first pair of biomarker values indicative of a             concentration of polynucleotide expression products of the             PLA2G7 gene and PLAC8 gene;         -   ii) a second pair of biomarker values indicative of a             concentration of polynucleotide expression products of the             CEACAM4 gene and LAMP1 gene;     -   c) at least one processing device that:         -   i) receives an indication of the pair of biomarker values             from the measuring device;         -   ii) determines an indicator using a ratio of the             concentration of the first and second polynucleotide             expression products using the biomarker values; and,         -   iii) compares the indicator to at least one indicator             reference; and,         -   iv) determines a likelihood of the subject having the at             least one medical condition using the results of the             comparison; and,         -   v) generates a representation of the indicator and the             likelihood for display to a user.

In another broad form the present invention seeks to provide a method for differentiating between inSIRS and ipSIRS in a biological subject, the method including:

-   -   a) obtaining a sample taken from a biological subject showing a         clinical sign of SIRS, the sample including polynucleotide         expression products;     -   b) in a measuring device:         -   i) amplifying at least some polynucleotide expression             products in the sample;         -   ii) determining an amplification amount representing a             degree of amplification required to obtain a defined level             of polynucleotide expression products including:             -   (1) amplification amounts for a first pair of                 polynucleotide expression products of the PLA2G7 gene                 and PLAC8 gene;             -   (2) amplification amounts for a second pair of                 polynucleotide expression products of the CEACAM4 gene                 and LAMP1 gene;     -   c) in a processing system:         -   i) retrieving the amplification amounts;         -   ii) determining an indicator by:             -   (1) determining a first derived biomarker value                 indicative of a ratio of concentrations of the first                 pair of polynucleotide expression products by                 determining a difference between the amplification                 amounts for the first pair;             -   (2) determining a second derived biomarker value                 indicative of a ratio of concentrations of the second                 pair of polynucleotide expression products by                 determining a difference between the amplification                 amounts for the second pair;             -   (3) determining the indicator by adding the first and                 second derived biomarker values;         -   iii) retrieving previously determined first and second             indicator references from a database, wherein the first and             second indicator references are distributions of indicators             determined for first and second groups of a reference             population, the first and second group consisting of             individuals diagnosed with inSIRS and ipSIRS respectively;         -   iv) comparing the indicator to the first and second             indicator references;         -   v) using the results of the comparison to determine a             probability of the subject being classified within the first             or second group;         -   vi) generating a representation at least partially             indicative of the indicator and the probability; and,         -   vii) providing the representation to a user to allow the             user to assess the likelihood of a biological subject having             at least one medical condition.

In another broad form the present invention seeks to provide a method for determining an indicator used in assessing a likelihood of a biological subject having a presence, absence, degree or prognosis of at least one medical condition, the method including:

-   -   a) determining a plurality of biomarker values, each biomarker         value being indicative of a value measured or derived for at         least one corresponding immune system biomarker of the         biological subject and being at least partially indicative of a         concentration of the immune system biomarker in a sample taken         from the subject;     -   b) determining the indicator using a combination of the         plurality of biomarker values, wherein:         -   i) at least two biomarkers have a mutual correlation in             respect of the at least one condition that lies within a             mutual correlation range, the mutual correlation range being             between ±0.9; and,         -   ii) the indicator has a performance value greater than or             equal to a performance threshold representing the ability of             the indicator to diagnose the presence, absence, degree or             prognosis of the at least one condition, the performance             threshold being indicative of an explained variance of at             least 0.3.

Typically the method includes:

-   -   c) determining a plurality of measured biomarker values, each         measured biomarker value being a measured value of a         corresponding biomarker of the biological subject; and,     -   d) determining the indicator by applying a function to at least         one of the measured biomarker values to determine at least one         derived biomarker value, the at least one derived biomarker         value being indicative of a value of a corresponding derived         biomarker.

Typically the function includes at least one of:

-   -   a) multiplying two biomarker values;     -   b) dividing two biomarker values;     -   c) adding two biomarker values;     -   d) subtracting two biomarker values;     -   e) a ratio of two biomarker values;     -   f) a weighted sum of at least two biomarker values;     -   g) a log sum of at least two biomarker values; and,     -   h) a sigmoidal function of at least two biomarker values.

Typically the method includes determining at least one derived biomarker value corresponding to a ratio of two measured biomarker values.

Typically the method includes combining at least two biomarker values to determine an indicator value representing the indicator.

Typically the method includes combining at least two biomarker values using a combining function, the combining function being at least one of:

-   -   a) an additive model;     -   b) a linear model;     -   c) a support vector machine;     -   d) a neural network model;     -   e) a random forest model;     -   f) a regression model;     -   g) a genetic algorithm;     -   h) an annealing algorithm;     -   i) a weighted sum;     -   j) a nearest neighbor model; and,     -   k) a probabilistic model.

Typically at least one of the at least two biomarkers is a derived biomarker.

Typically the method includes:

-   -   a) determining a first derived biomarker value, the first         derived biomarker value being indicative of a ratio of         concentrations of the first and second immune system biomarkers;     -   b) determining a second derived biomarker value, the second         derived biomarker value being indicative of a ratio of         concentrations of the third and fourth measured immune system         biomarkers; and,     -   c) adding the first and second derived biomarker values to         generate an indicator value.

Typically the method is performed at least in part using an electronic processing device.

Typically the method includes, in the electronic processing device:

-   -   a) receiving a plurality of measured biomarker values, each         measured biomarker value being a measured value of a         corresponding immune system biomarker;     -   b) applying a function to at least one of the measured biomarker         values to determine at least one derived biomarker value, the at         least one derived biomarker value being indicative of a value of         a corresponding derived biomarker; and,     -   c) combining at least one derived biomarker value and at least         one other biomarker value to determine the indicator.

Typically the mutual correlation range is at least one of:

-   -   a) ±0.8;     -   b) ±0.7;     -   c) ±0.6;     -   d) ±0.5;     -   e) ±0.4;     -   f) ±0.3;     -   g) ±0.2; and,     -   h) ±0.1.

Typically each biomarker has a condition correlation with the presence, absence, degree or prognosis of the at least one condition that lies outside a condition correlation range, the condition correlation range being between ±0.3.

Typically the condition correlation range is at least one of:

-   -   a) ±0.9;     -   b) ±0.8;     -   c) ±0.7;     -   d) ±0.6;     -   e) ±0.5; and,     -   f) ±0.4.

Typically the performance threshold is indicative of an explained variance of at least one of:

-   -   a) 0.4;     -   b) 0.5;     -   c) 0.6;     -   d) 0.7;     -   e) 0.8; and,     -   f) 0.9.

Typically the biomarker value is indicative of a level or abundance of a molecule selected from one or more of a nucleic acid molecule and a proteinaceous molecule.

Typically the method includes generating a representation of the indicator.

Typically the representation includes:

-   -   a) an alphanumeric indication of the indicator;     -   b) a graphical indication of a comparison of the indicator to         one or more indicator references;     -   c) an alphanumeric indication of a likelihood of the subject         having at least one medical condition.

Typically the method includes:

-   -   a) comparing the indicator to an indicator reference; and,     -   b) determining a likelihood in accordance with results of the         comparison.

Typically the indicator reference is based on at least one of:

-   -   a) an indicator threshold range;     -   b) an indicator threshold; and,     -   c) an indicator distribution.

Typically the indicator reference is derived from indicators determined for a number of individuals in a reference population.

Typically the indicator reference is based on a distribution of indicators determined for a group of a reference population, the group consisting of individuals diagnosed with the medical condition.

Typically the reference population includes:

-   -   a) a plurality of individuals of different sexes;     -   b) a plurality of individuals of different ethnicities;     -   c) a plurality of healthy individuals;     -   d) a plurality of individuals suffering from at least one         diagnosed medical condition;     -   e) a plurality of individuals showing clinical signs of at least         one medical condition; and,     -   f) first and second groups of individuals, each group of         individuals suffering from a respective diagnosed medical         condition.

Typically the indicator is for use in determining the likelihood that a biological subject has at least one medical condition, and wherein the reference population includes:

-   -   a) individuals presenting with clinical signs of the at least         one medical condition;     -   b) individuals diagnosed with the at least one medical         condition; and,     -   c) healthy individuals.

Typically the indicator reference is retrieved from a database.

Typically the likelihood is based on a probability generated using the results of the comparison.

Typically the indicator is for determining a likelihood of the subject having a first or second condition, and wherein the method includes:

-   -   a) comparing the indicator to first and second indicator         references, the first and second indicator references being         indicative of first and second conditions; and,     -   b) determining the likelihood in accordance with the results of         the comparison.

Typically the method includes:

-   -   a) determining first and second indicator probabilities using         the results of the comparisons; and,     -   b) combining the first and second indicator probabilities to         determine a condition probability indicative of the likelihood.

Typically the first and second indicator references are distributions of indicators determined for first and second groups of a reference population, the first and second group consisting of individuals diagnosed with the first or second condition respectively.

Typically the method includes:

-   -   a) obtaining a sample taken from the biological subject, the         sample including polynucleotide expression products;     -   b) quantifying at least some of the polynucleotide expression         products within the sample to determine at least a pair of         biomarker values;     -   c) determining the indicator at least in part using the pair of         biomarker values;

Typically the method includes, determining the indicator at least in part using a ratio of concentrations of the polynucleotide expression products.

Typically the method includes:

-   -   a) quantifying polynucleotide expression products by:     -   b) amplifying at least some polynucleotide expression products         in the sample; and,     -   c) determining an amplification amount representing a degree of         amplification required to obtain a defined level of each of a         pair of polynucleotide expression products; and,     -   d) determining the indicator by determining a difference between         the amplification amounts.

Typically the amplification amount is at least one of:

-   -   a) a cycle time;     -   b) a number of cycles;     -   c) a cycle threshold;     -   d) an amplification time; and,     -   e) relative to an amplification amount of another amplified         product.

Typically the method includes determining:

-   -   a) a first derived biomarker value by determining a difference         between the amplification amounts of a first pair of         polynucleotide expression products;     -   b) a second derived biomarker value by determining a difference         between the amplification amounts of a second pair of         polynucleotide expression products;     -   c) determining the indicator by adding the first and second         derived biomarker values.

Typically the immune system biomarker is an IRS biomarker of an immune system of the biological subject that is altered, or whose level of expression is altered, as part of an inflammatory response to damage or pathogenic insult.

Typically the indicator is for determining a likelihood of the subject having at least one of inSIRS and ipSIRS, and wherein the method includes:

-   -   a) determining a first pair of biomarker values indicative of a         concentration of polynucleotide expression products of the         PLA2G7 gene and PLAC8 gene;     -   b) determining a second pair of biomarker values indicative of a         concentration of polynucleotide expression products of the         CEACAM4 gene and LAMP1 gene; and,     -   c) determining the indicator using the first and second pairs of         biomarker values.

Typically the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein the method includes:

-   -   a) determining a first pair of biomarker values indicative of a         concentration of polynucleotide expression products of the         PLA2G7 gene and PLAC8 gene;     -   b) determining a second pair of biomarker values indicative of a         concentration of polynucleotide expression products of the         CEACAM4 gene and LAMP1 gene; and,     -   c) determining the indicator using the first and second pairs of         biomarker values.

Typically the indicator is for determining a likelihood of the subject having inSIRS or a healthy condition, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

-   -   a) the first IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         A IRS immune system biomarker genes; and     -   b) the second IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         B IRS immune system biomarker genes.

Typically the indicator is for determining a likelihood of the subject having ipSIRS or a healthy condition, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

-   -   a) the first IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         C IRS immune system biomarker genes; and,     -   b) the second IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         D IRS immune system biomarker genes.

Typically the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

-   -   a) the first IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         E IRS immune system biomarker genes; and,     -   b) the second IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         F IRS immune system biomarker genes.

Typically the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first, second, third and fourth IRS immune system biomarker groups, wherein:

-   -   a) the first IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         G IRS immune system biomarker genes;     -   b) the second IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         H IRS immune system biomarker genes;     -   c) the third IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         I IRS immune system biomarker genes; and,     -   d) the fourth IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         J IRS immune system biomarker genes.

Typically the first IRS immune system biomarker is a PLA2G7 expression product, the second IRS immune system biomarker is a PLAC8 expression product, the third IRS immune system biomarker is a CEACAM4 expression product and the fourth IRS immune system biomarker is a LAMP1 expression product.

Typically the indicator is for determining a likelihood of the subject having mild sepsis or severe sepsis, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

-   -   a) the first IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         K IRS immune system biomarker genes; and,     -   b) the second IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         L IRS immune system biomarker genes.

Typically the indicator is for determining a likelihood of the subject having mild sepsis or septic shock, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

-   -   a) the first IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         M IRS immune system biomarker genes; and,     -   b) the second IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         N IRS immune system biomarker genes.

Typically the indicator is for determining a likelihood of the subject having severe sepsis or septic shock, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

-   -   a) the first IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         O IRS immune system biomarker genes; and,     -   b) the second IRS immune system biomarker group consists of         polynucleotide and/or polypeptide expression products from group         P IRS immune system biomarker genes.

In another broad form the present invention seeks to provide apparatus for determining an indicator used in assessing a likelihood of a biological subject having a presence, absence, degree or prognosis of at least one medical condition, the apparatus including a processing device that:

-   -   a) determines a plurality of biomarker values, each biomarker         value being indicative of a value measured or derived for at         least one corresponding immune system biomarker of the         biological subject and being at least partially indicative of a         concentration of the immune system biomarker in a sample taken         from the subject;     -   b) determines the indicator using a combination of the plurality         of biomarker values, wherein:         -   i) at least two biomarkers have a mutual correlation in             respect of the at least one condition that lies within a             mutual correlation range, the mutual correlation range being             between ±0.9; and,         -   ii) the indicator has a performance value greater than or             equal to a performance threshold representing the ability of             the indicator to diagnose the presence, absence, degree or             prognosis of the at least one condition, the performance             threshold being indicative of an explained variance of at             least 0.3.

In one broad form the present invention seeks to provide a method for determining an indicator for use in diagnosing the presence, absence, degree or prognosis of at least one condition in a biological subject, the method including:

-   -   a) determining a plurality of biomarker values, each biomarker         value being indicative of a value measured or derived for at         least one corresponding biomarker of the biological subject;     -   b) determining the indicator using a combination of the         plurality of biomarker values, the at least one indicator being         at least partially indicative of the presence, absence, degree         or prognosis of the at least one condition, wherein:         -   i) at least two markers have a mutual correlation in respect             of the at least one condition that lies within a mutual             correlation range, the mutual correlation range being             between ±0.9; and,         -   ii) the indicator has a performance value greater than or             equal to a performance threshold representing the ability of             the indicator to diagnose the presence, absence, degree or             prognosis of the at least one condition, the performance             threshold being indicative of an explained variance of at             least 0.3.

Typically the method includes:

-   -   a) determining a plurality of measured biomarker values, each         measured biomarker value being a measured value of a         corresponding biomarker of the biological subject; and,     -   b) applying a function to at least one of the measured biomarker         values to determine at least one derived biomarker value, the at         least one derived biomarker value being indicative of a value of         a corresponding derived biomarker.

Typically the function includes at least one of:

-   -   a) multiplying two biomarker values;     -   b) dividing two biomarker values;     -   c) adding two biomarker values;     -   d) subtracting two biomarker values;     -   e) a weighted sum of at least two biomarker values;     -   f) a log sum of at least two biomarker values; and,     -   g) a sigmoidal function of at least two biomarker values.

Typically the method includes determining at least one derived biomarker value corresponding to a ratio of two measured biomarker values.

Typically the method includes combining at least two biomarker values to determine an indicator value representing the indicator.

Typically the method includes combining at least two biomarker values using a combining function, the combining function being at least one of:

-   -   a) an additive model;     -   b) a linear model;     -   c) a support vector machine;     -   d) a neural network model;     -   e) a random forest model;     -   f) a regression model;     -   g) a genetic algorithm;     -   h) an annealing algorithm;     -   i) a weighted sum;     -   j) a nearest neighbor model; and,     -   k) a probabilistic model.

Typically at least one of the at least two biomarkers is a derived biomarker.

Typically the method includes:

-   -   a) determining a first derived biomarker value, the first         derived biomarker value being a ratio of first and second         measured biomarker values;     -   b) determining a second derived biomarker value, the second         derived biomarker value being a ratio of third and fourth         measured biomarker values; and,     -   c) adding the first and second derived biomarker values to         generate an indicator value.

Typically the method includes:

-   -   a) determining an indicator value;     -   b) comparing the indicator value to at least one indicator value         range; and,     -   c) using a result of the comparison in diagnosing the presence,         absence, degree or prognosis of at least one condition.

Typically the method is performed at least in part using an electronic processing device.

Typically the method includes, in the electronic processing device:

-   -   a) receiving a plurality of measured biomarker values, each         measured biomarker value being a measured value of a         corresponding biomarker of the biological subject;     -   b) applying a function to at least one of the measured biomarker         values to determine at least one derived biomarker value, the at         least one derived biomarker value being indicative of a value of         a corresponding derived biomarker; and,     -   c) combining at least one derived biomarker value and at least         one other biomarker value to determine an indicator value.

Typically the method includes generating a representation in accordance with the at least one indicator value.

Typically the method includes:

-   -   a) comparing the indicator value to at least one indicator value         range; and,     -   b) displaying a result of the comparison.

Typically the mutual correlation range is at least one of:

-   -   a) ±0.8;     -   b) ±0.7;     -   c) ±0.6;     -   d) ±0.5;     -   e) ±0.4;     -   f) ±0.3;     -   g) ±0.2; and,     -   h) ±0.1.

Typically each biomarker has a condition correlation with the presence, absence, degree or prognosis of the at least one condition that lies outside a condition correlation range, the condition correlation range being between ±0.3.

Typically the condition correlation range is at least one of:

-   -   a) ±0.9;     -   b) ±0.8;     -   c) ±0.7;     -   d) ±0.6;     -   e) ±0.5; and,     -   f) ±0.4.

Typically the performance threshold is indicative of an explained variance of at least one of:

-   -   a) 0.4;     -   b) 0.5;     -   c) 0.6;     -   d) 0.7;     -   e) 0.8; and,     -   f) 0.9.

Typically the biomarker value is indicative of a level or abundance of a molecule or entity selected from one or more of:

-   -   a) A nucleic acid molecule;     -   b) A proteinaceous molecule;     -   c) An amino acid     -   d) A carbohydrate;     -   e) A lipid;     -   f) A steroid;     -   g) An inorganic molecule;     -   h) An ion;     -   i) A drug;     -   j) A chemical;     -   k) A metabolite;     -   l) A toxin;     -   m) A nutrient;     -   n) A gas;     -   o) A cell;     -   p) A pathogenic organism; and,     -   q) A non-pathogenic organism.

In another broad form the present invention seeks to provide apparatus for determining an indicator for use in diagnosing the presence, absence, degree or prognosis of at least one condition in a biological subject, the apparatus including an electronic processing device that:

-   -   a) determines a plurality of biomarker values, each biomarker         value being indicative of a value measured or derived for at         least one corresponding biomarker of the biological subject;     -   b) determines the indicator using a combination of the plurality         of biomarker values, the at least one indicator being at least         partially indicative of the presence, absence, degree or         prognosis of the at least one condition, wherein:         -   i) at least two biomarkers have a mutual correlation in             respect of the at least one condition that lies within a             mutual correlation range, the mutual correlation range being             between ±0.9; and,         -   ii) the indicator has a performance value greater than or             equal to a performance threshold representing the ability of             the indicator to diagnose the presence, absence, degree or             prognosis of the at least one condition, the performance             threshold being indicative of an explained variance of at             least 0.3.

In another broad form the present invention seeks to provide a diagnostic signature for use in diagnosing the presence, absence, degree or prognosis of at least one condition in a biological subject, the diagnostic signature defining a combination of at least two biomarker values corresponding to values of biomarkers that can be measured for or derived from the biological subject, wherein:

-   -   a) at least two biomarkers have a mutual correlation for the at         least one condition that lies within a mutual correlation range,         the mutual correlation range being between ±0.9; and,     -   b) the combination of at least two biomarker values has a         performance value greater than or equal to a performance         threshold representing the ability of the combination of the at         least two biomarkers to diagnose the presence, absence, degree         or prognosis of the at least one condition, the performance         threshold being a variance explained of at least 0.3.

Typically the diagnostic signature defines a function to be applied to at least one measured biomarker value to determine at least one derived biomarker value, the at least one derived biomarker value being indicative of a value of a corresponding derived biomarker.

Typically the function includes at least one of:

-   -   a) multiplying two biomarker values;     -   b) dividing two biomarker values;     -   c) adding two biomarker values;     -   d) subtracting two biomarker values;     -   e) a weighted sum of at least two biomarker values;     -   f) a log sum of at least two biomarker values; and,     -   g) a sigmoidal function of at least two biomarker values.

Typically the at least one derived biomarker value corresponds to a ratio of two measured biomarker values.

Typically the diagnostic signature defines a combination of at least two biomarker values for determining an indicator value representing the indicator.

Typically the diagnostic signature defines a combining function for combining at least two biomarker values, the combining function being at least one of:

-   -   a) an additive model;     -   b) a linear model;     -   c) a support vector machine;     -   d) a neural network model;     -   e) a random forest model;     -   f) a regression model;     -   g) a genetic algorithm;     -   h) an annealing algorithm; and,     -   i) a weighted sum.

Typically at least one of the at least two biomarkers is a derived biomarker.

Typically the diagnostic signature defines:

-   -   a) a first derived biomarker value, the first derived biomarker         value being a ratio of first and second measured biomarker         values;     -   b) a second derived biomarker value, the second derived         biomarker value being a ratio of third and fourth measured         biomarker values; and,     -   c) a combination of the first and second derived biomarker         values to generate an indicator value.

Typically the diagnostic signature defines at least one indicator value range and wherein comparison of at least one indicator value to the at least one indicator value range is used in diagnosing the presence, absence, degree or prognosis of at least one condition.

Typically the mutual correlation range is at least one of:

-   -   a) ±0.8;     -   b) ±0.7;     -   c) ±0.6;     -   d) ±0.5;     -   e) ±0.4;     -   f) ±0.3;     -   g) ±0.2; and,     -   h) ±0.1.

Typically each biomarker has a condition correlation with the presence, absence, degree or prognosis of the at least one condition that lies outside a condition correlation range, the condition correlation range being between ±0.3.

Typically the condition correlation range is at least one of:

-   -   a) ±0.9;     -   b) ±0.8;     -   c) ±0.7;     -   d) ±0.6;     -   e) ±0.5; and,     -   f) ±0.4.

Typically the performance threshold is indicative of an explained variance of at least one of:

-   -   a) 0.4;     -   b) 0.5;     -   c) 0.6;     -   d) 0.7;     -   e) 0.8; and,     -   f) 0.9.

Typically the biomarker value is indicative of a level or abundance of a molecule or entity selected from one or more of:

-   -   a) A nucleic acid molecule;     -   b) A proteinaceous molecule;     -   c) An amino acid     -   d) A carbohydrate;     -   e) A lipid;     -   f) A steroid;     -   g) An inorganic molecule;     -   h) An ion;     -   i) A drug;     -   j) A chemical;     -   k) A metabolite;     -   l) A toxin;     -   m) A nutrient;     -   n) A gas;     -   o) A cell;     -   p) A pathogenic organism; and,     -   q) A non-pathogenic organism.

In another broad form the present invention seeks to provide a method of identifying biomarkers for use in a diagnostic signature, the diagnostic signature being for use in diagnosing the presence, absence, degree or prognosis of at least one condition in a biological subject, the method including:

-   -   a) for a number of candidate biomarkers, ranking the candidate         biomarkers in accordance with the ability of each biomarker to         distinguish between the presence, absence, degree or prognosis         of at least one condition in a biological subject;     -   b) selecting at least two candidate biomarkers in accordance         with the ranking, the at least two biomarkers having a mutual         correlation for the at least one condition that lies within a         mutual correlation range, the mutual correlation range being         between ±0.9;     -   c) determining a performance value of a combination of the at         least two candidate biomarkers; and,     -   d) defining a diagnostic signature in accordance with the         combination of the at least two biomarkers if the performance         value is greater than or equal to a performance threshold         representing the ability of the indicator to diagnose the         presence, absence, degree or prognosis of the at least one         condition, the performance threshold being indicative of an         explained variance of at least 0.3.

Typically the method includes determining a combination of at least two candidate biomarkers using a combining function, the combining function being at least one of:

-   -   a) an additive model;     -   b) a linear model;     -   c) a support vector machine;     -   d) a neural network model;     -   e) a random forest model;     -   f) a regression model;     -   g) a genetic algorithm;     -   h) an annealing algorithm; and,     -   i) a weighted sum.

Typically the method includes:

-   -   a) selecting a next combining function;     -   b) determining if a performance value of a combination of the at         least two candidate biomarkers determined by the next combining         function is greater than or equal to a performance threshold;         and,     -   c) if the performance value is not greater than or equal to a         performance threshold, repeating steps a) and b) for successive         combining functions.

Typically the method includes:

-   -   a) selecting two candidate biomarkers;     -   b) determining if a performance value of a combination of the         two candidate biomarkers is greater than or equal to a         performance threshold; and,     -   c) if the performance value is not greater than or equal to a         performance threshold:         -   i) combining the selected candidate biomarkers with at least             one additional candidate biomarker; and,         -   ii) repeating steps a) and b) with at least one additional             candidate biomarker.

Typically the method includes combining a number of candidate biomarkers up to a limit.

Typically the method includes:

-   -   a) selecting a highest ranked candidate biomarker;     -   b) selecting a next highest ranked candidate biomarker;     -   c) for the selected candidate markers, determining if the mutual         correlation for the candidate biomarkers within the mutual         correlation range; and,     -   d) if not, repeating steps b) and c) until two candidate         biomarkers are selected having a mutual correlation within the         mutual correlation range.

Typically the method includes:

-   -   a) defining at least two groups of candidate biomarkers,         candidate biomarkers in different groups having a mutual         correlation within the mutual correlation range;     -   b) ranking the candidate biomarkers in each group; and,     -   c) selecting candidate biomarkers from the different groups.

Typically the method includes:

-   -   a) using reference data for at least one individual to define a         number of groups indicative of the presence, absence, degree or         prognosis of the at least one condition; and,     -   b) using at least one analysis technique to identify a number of         candidate biomarkers that are potentially useful for         distinguishing the groups.

Typically the method includes using reference values measured for reference biomarkers for the at least one individual to identify the candidate biomarkers.

Typically the method includes using reference values to filter reference biomarkers to determine candidate biomarkers.

Typically at least one of the candidate biomarkers is a derived biomarker derived from at least one of the reference biomarkers using a function.

Typically the derived biomarkers are derived from filtered biomarkers.

Typically the method includes:

-   -   a) applying a function to at least one of the reference values         to determine at least one derived reference biomarker value, the         at least one derived reference biomarker value being indicative         of a value of a corresponding derived reference biomarker; and,     -   b) determining at least one candidate biomarker using the at         least one derived reference biomarker value.

Typically the method includes:

-   -   a) using reference data for at least one individual to define a         number of groups indicative of the presence, absence, degree or         prognosis of the at least one condition; and,     -   b) for each group, combining a range of at least two reference         biomarker values to determine an indicator value range for the         group.

Typically the mutual correlation range is at least one of:

-   -   a) ±0.8;     -   b) ±0.7;     -   c) ±0.6;     -   d) ±0.5;     -   e) ±0.4;     -   f) ±0.3;     -   g) ±0.2; and,     -   h) ±0.1.

Typically each biomarker has a condition correlation with the presence, absence, degree or prognosis of the at least one condition that lies outside a condition correlation range, the condition correlation range being between ±0.3.

Typically the condition correlation range is at least one of:

-   -   a) ±0.9;     -   b) ±0.8;     -   c) ±0.7;     -   d) ±0.6;     -   e) ±0.5; and,     -   f) ±0.4.

Typically the performance threshold is indicative of an explained variance of at least one of:

-   -   a) 0.4;     -   b) 0.5;     -   c) 0.6;     -   d) 0.7;     -   e) 0.8; and,     -   f) 0.9.

In another broad form the present invention seeks to provide apparatus for identifying markers for use in a diagnostic signature, the diagnostic signature being for use in diagnosing the presence, absence, degree or prognosis of at least one condition in a biological subject, the apparatus including an electronic process device that:

-   -   a) for a number of candidate biomarkers, ranks the candidate         biomarkers in accordance with the ability of each biomarker to         distinguish between the presence, absence, degree or prognosis         of at least one condition in a biological subject;     -   b) selects at least two candidate biomarkers in accordance with         the ranking, the at least two biomarkers having a mutual         correlation for the at least one condition that lies within a         mutual correlation range, the mutual correlation range being         between ±0.9;     -   c) determines a performance value of a combination of the at         least two candidate biomarkers; and,     -   d) defines a diagnostic signature in accordance with the         combination of the at least two biomarkers if the performance         value is greater than or equal to a performance threshold         representing the ability of the indicator to diagnose the         presence, absence, degree or prognosis of the at least one         condition, the performance threshold being indicative of an         explained variance of at least 0.3.

In another broad form the present invention seeks to provide a method for diagnosing the presence or absence of inSIRS or a healthy condition in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from inSIRS and a healthy condition, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group A IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group B IRS biomarker genes.

In another broad form the present invention seeks to provide a method for diagnosing the presence or absence of ipSIRS or a healthy condition in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from ipSIRS and a healthy condition, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group C IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group D IRS biomarker genes.

In another broad form the present invention seeks to provide a method for diagnosing the presence or absence of inSIRS or ipSIRS in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from inSIRS and ipSIRS, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group E IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group F IRS biomarker genes.

In another broad form the present invention seeks to provide a method for diagnosing the presence or absence of inSIRS or ipSIRS in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from inSIRS and ipSIRS, wherein: (i) at least four IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least four IRS biomarkers is selected from a first IRS biomarker group, wherein at least one other of the at least four IRS biomarkers is selected from a second IRS biomarker group, wherein at least one other of the at least four IRS biomarkers is selected from a third IRS biomarker group, and wherein at least one other of the at least four IRS biomarkers is selected from a fourth IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group G IRS biomarker genes, wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group H IRS biomarker genes, wherein the third IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group I IRS biomarker genes and wherein the fourth IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group J IRS biomarker genes.

Suitably, the first IRS biomarker is a PLA2G7 expression product, the second IRS biomarker is a PLAC8 expression product, the third IRS biomarker is a CEACAM4 expression product and the fourth IRS biomarker is a LAMP1 expression product.

In another broad form the present invention seeks to provide a method for diagnosing the presence or absence of mild sepsis or severe sepsis in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from mild sepsis and severe sepsis, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group K IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group L IRS biomarker genes.

In another form the present invention seeks to provide a method for diagnosing the presence or absence of mild sepsis or septic shock in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from mild sepsis and septic shock, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group M IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group N IRS biomarker genes.

In another form the present invention seeks to provide a method for diagnosing the presence or absence of severe sepsis or septic shock in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from severe sepsis and septic shock, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group O IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group P IRS biomarker genes.

In another broad form the present invention seeks to provide a kit comprising: (i) a reagent that allows quantification of a first IRS biomarker; and (ii) a reagent that allows quantification of a second IRS biomarker, wherein the first and second IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a stage of ipSIRS selected from mild sepsis, severe sepsis and septic shock, which at least one condition lies within a mutual correlation range of between ±0.9, and wherein a combination of respective biomarker values for the first and second IRS biomarkers that are measured for or derived from a biological subject has a performance value greater than or equal to a performance threshold representing the ability of the combination of the first and second IRS biomarkers to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being a variance explained of at least 0.3.

Suitably, the kit further comprises: (iii) a reagent that allows quantification of a third IRS biomarker; and (iv) a reagent that allows quantification of a fourth IRS biomarker, wherein the third and fourth IRS biomarkers have a mutual correlation in respect of at the least one condition that lies within a mutual correlation range of between ±0.9, and wherein a combination of respective biomarker values for the third and fourth IRS biomarkers that are measured for or derived from a biological subject has a performance value greater than or equal to a performance threshold representing the ability of the combination of the third and fourth IRS biomarkers to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being a variance explained of at least 0.3.

Suitably, the kit is for diagnosing the presence or absence of inSIRS or a healthy condition, wherein the first IRS biomarker is selected from a first IRS biomarker group and wherein the second IRS biomarker is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group A IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group B IRS biomarker genes.

Suitably, the kit is for diagnosing the presence or absence of ipSIRS or a healthy condition, wherein the first IRS biomarker is selected from a first IRS biomarker group and wherein the second IRS biomarker is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group C IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group D IRS biomarker genes.

Suitably, the kit is for diagnosing the presence or absence of inSIRS or ipSIRS, wherein the first IRS biomarker is selected from a first IRS biomarker group and wherein the second IRS biomarker is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group E IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group F IRS biomarker genes.

Suitably, the kit is for diagnosing the presence or absence of inSIRS or ipSIRS, wherein the first IRS biomarker is selected from a first IRS biomarker group, wherein the second IRS biomarker is selected from a second IRS biomarker group, wherein the third IRS biomarker is selected from a third IRS biomarker group and wherein the fourth IRS biomarker is selected from a fourth IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group G IRS biomarker genes, wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group H IRS biomarker genes, wherein the third IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group I IRS biomarker genes and wherein the fourth IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group J IRS biomarker genes.

Suitably, the first IRS biomarker is a PLA2G7 expression product, the second IRS biomarker is a PLAC8 expression product, the third IRS biomarker is a CEACAM4 expression product and the fourth IRS biomarker is a LAMP1 expression product.

Suitably, the kit is for diagnosing the presence or absence of mild sepsis or severe sepsis, wherein the first IRS biomarker is selected from a first IRS biomarker group and wherein the second IRS biomarker is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group K IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group L IRS biomarker genes.

Suitably, the kit is for diagnosing the presence or absence of mild sepsis or septic shock, wherein the first IRS biomarker is selected from a first IRS biomarker group and wherein the second IRS biomarker is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group M IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group N IRS biomarker genes.

Suitably, the kit is for diagnosing the presence or absence of severe sepsis or septic shock, wherein the first IRS biomarker is selected from a first IRS biomarker group and wherein the second IRS biomarker is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group O IRS biomarker genes and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group P IRS biomarker genes.

In another broad form the present invention seeks to provide a method for treating, preventing or inhibiting the development of at least one condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis or septic shock) in a subject, the method comprising (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining an indicator using a combination of the plurality of IRS biomarker values, the indicator being at least partially indicative of the presence, absence or degree of the at least one condition, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; and (c) administering to the subject, on the basis that the indicator indicates the presence of inSIRS, an effective amount of an agent that treats or ameliorates the symptoms or reverses or inhibits the development of inSIRS, or administering to the subject, on the basis that the indicator indicates the presence of ipSIRS or a particular stage of ipSIRS, an effective amount of an agent that treats or ameliorates the symptoms or reverses or inhibits the development of ipSIRS or the particular stage of ipSIRS.

Suitably, the method further comprises: (1) determining a plurality of measured IRS biomarker values, each measured IRS biomarker value being a measured value of an IRS biomarker of the biological subject; and (2) applying a function to at least one of the measured IRS biomarker values to determine at least one derived IRS biomarker value, the at least one derived IRS biomarker value being indicative of a value of a corresponding derived IRS biomarker.

Suitably, the function includes at least one of: (a) multiplying two IRS biomarker values; (b) dividing two IRS biomarker values; (c) adding two IRS biomarker values; (d) subtracting two IRS biomarker values; (e) a weighted sum of at least two IRS biomarker values; (f) a log sum of at least two IRS biomarker values; and (g) a sigmoidal function of at least two IRS biomarker values.

In another broad form the present invention seeks to provide a method of monitoring the efficacy of a particular treatment regimen in a subject towards a desired health state, the method comprising: a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject after treatment with a treatment regimen; (b) determining an indicator using a combination of the plurality of IRS biomarker values, the indicator being at least partially indicative of the presence, absence or degree of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, and (c) determining that the treatment regimen is effective for changing the health status of the subject to the desired health state on the basis that the indicator indicates the presence of a healthy condition or the presence of a condition of a lower degree relative to the degree of the condition in the subject before treatment with the treatment regimen.

In another broad form the present invention seeks to provide a method of correlating a biomarker signature with an effective treatment regimen for a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), the method comprising: (a) determining a biomarker signature defining a combination of at least two IRS biomarker values corresponding to values of at least two IRS biomarkers that can be measured for or derived from a biological subject having the condition and for whom an effective treatment has been identified, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of the condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the condition, or to provide a prognosis for the condition, the performance threshold being indicative of an explained variance of at least 0.3; and (b) correlating the biomarker signature so determined with an effective treatment regimen for the condition.

In another broad form the present invention seeks to provide a method of determining whether a treatment regimen is effective for treating a subject with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), the method comprising: (a) determining a plurality of post-treatment IRS biomarker values, each post-treatment IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject after treatment with the treatment regimen; (b) determining a post-treatment indicator using a combination of the plurality of post-treatment IRS biomarker values, the post-treatment indicator being at least partially indicative of the presence, absence or degree of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, wherein: (i) at the least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the post-treatment indicator has a performance value greater than or equal to a performance threshold representing the ability of the post-treatment indicator to diagnose the presence, absence or degree of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein the post-treatment indicator indicates whether the treatment regimen is effective for treating the condition in the subject on the basis that post-treatment indicator indicates the presence of a healthy condition or the presence of a condition of a lower degree relative to the degree of the condition in the subject before treatment with the treatment regimen.

In another broad form the present invention seeks to provide a method of correlating a biomarker signature with a positive or negative response or a side effect to a treatment regimen, the method comprising: (a) determining a biomarker signature defining a combination of at least two IRS biomarker values corresponding to values of at least two IRS biomarkers that can be measured for or derived from a biological subject following commencement of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; and (b) correlating the biomarker signature so determined with a positive or negative response to the treatment regimen.

In another broad form the present invention seeks to provide a method of determining a positive or negative response to a treatment regimen and/or a side effect of a treatment regimen by a subject with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), the method: (a) correlating a reference biomarker signature with a positive or negative response or a side effect to the treatment regimen, wherein the biomarker signature defines a combination of at least two IRS biomarker values corresponding to values of at least two IRS biomarkers that are measured for or derived from a control biological subject or control group, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; (b) determining a sample biomarker signature defining a combination of at least two IRS biomarker values corresponding to values of at least two IRS biomarkers that are measured for or derived from a biological subject following commencement of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; wherein the sample biomarker signature indicates whether the subject is responding positively or negatively to the treatment regimen and/or is developing a side effect from the treatment regimen, based on the correlation of the reference biomarker signature with the positive or negative response or side effect to the treatment regimen.

In another broad form the present invention seeks to provide a method of determining a positive or negative response to a treatment regimen and/or a side effect to a treatment regimen by a biological subject, the method comprising: (a) determining a sample biomarker signature defining a combination of at least two IRS biomarker values corresponding to values of at least two IRS biomarkers that are measured for or derived from a biological subject following commencement of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein the sample biomarker signature is correlated with a positive or negative response to the treatment regimen and/or to a side effect from the treatment regimen; and (b) determining whether the subject is responding positively or negatively to the treatment regimen and/or is developing a side effect from the treatment regimen based on the sample biomarker signature.

BRIEF DESCRIPTION OF THE DRAWINGS

An example of the present invention will now be described with reference to the accompanying drawings, in which: —

FIG. 1A is a flowchart of an example of a method for deriving an indicator for use in diagnosing the presence, absence or degree of at least one condition or in providing a prognosis of at least one condition in a biological subject;

FIG. 1B is a flowchart of an example of a method for identifying biomarkers for use in a biomarker signature;

FIG. 2 is a schematic diagram of an example of a distributed computer architecture;

FIG. 3 is a schematic diagram of an example of a processing system of FIG. 2;

FIG. 4 is a schematic diagram of an example of a computer system of FIG. 2;

FIG. 5 is a flowchart of a specific example of a method for identifying biomarkers for use in a biomarker signature;

FIG. 6A is a flowchart of a first example of a method for selecting candidate biomarkers;

FIG. 6B is a flowchart of a second example of a method for selecting candidate biomarkers;

FIG. 7 is a flowchart of a second example of a method for use in diagnosing the presence, absence or degree of at least one condition or in providing a prognosis of at least one condition in a biological subject;

FIG. 8A is a plot of 941 mRNA biomarkers against the AUC for differentiating between healthy condition and post-surgical inflammation (PS) (also referred to herein as “infection-negative SIRS” (inSIRS)), for individual biomarkers having an AUC greater than 0.7;

FIG. 8B is a box and whisker plot showing the best mRNA biomarker for separating healthy condition and PS;

FIG. 8C is a plot of the AUC for the diagnostic ability of 1000 derived biomarkers in separating healthy condition and PS with all derived biomarkers having an AUC of 1.0;

FIG. 8D is a box and whisker plot of the best performing derived biomarker, based on AUC, for separating healthy condition and PS;

FIGS. 8E and 8F are two plots showing the correlation to each other of the biomarkers in each group;

FIGS. 8G and 8H are two plots demonstrating the AUC of the biomarkers in each group (groups 1 and 2);

FIG. 8I is a box and whisker plot showing that when biomarkers are derived from group 1 and group 2 that a greater overall AUC is obtained;

FIG. 9A is a plot of the 941 mRNA biomarkers against the AUC for differentiating between healthy condition and sepsis (also referred to herein as “infection-positive SIRS” (ipSIRS)) with all individual biomarkers having an AUC greater than 0.7;

FIG. 9B is a box and whisker plot showing the best mRNA biomarker for separating healthy condition and sepsis;

FIG. 9C is a plot of the AUC for the diagnostic ability of 1000 derived biomarkers in separating healthy condition and sepsis with all derived biomarkers having an AUC of 1.0;

FIG. 9D is a box and whisker plot of the best performing derived biomarker, based on AUC, for separating healthy condition and sepsis;

FIGS. 9E and 9F show the correlation to each other of the biomarkers groups correlated to each group;

FIGS. 9G and 9H are two plots demonstrating the AUC of the biomarkers in each group (bucketgroups 1 and 2);

FIG. 9I is a box and whisker plot showing that when biomarkers are derived from bucketgroup 1 and bucketgroup 2 that a greater overall AUC is obtained;

FIG. 10A is a plot of the 359 mRNA biomarkers against the AUC for differentiating between PS and sepsis with all individual biomarkers having had an AUC greater than 0.7;

FIG. 10B is a box and whisker plot showing the best mRNA biomarker for separating PS and sepsis;

FIG. 10C is a plot of the AUC for the diagnostic ability of 1000 derived biomarkers in separating PS and sepsis with all derived biomarkers having an AUC of 0.9;

FIG. 10D is a box and whisker plot of the best derived biomarker for separating PS and sepsis;

FIG. 10E is a plot showing the correlation of the biomarkers in each bucketgroup to the condition;

FIG. 10F is a box and whisker plot showing that when biomarkers are derived from bucketgroup 1 and bucketgroup 2 that a greater overall AUC is obtained;

FIG. 10G is a plot demonstrating the AUC of the biomarkers in each of four bucketgroups;

FIG. 10H is a box and whisker plot showing that when biomarkers are derived from each of the four bucketgroups a greater overall AUC is obtained;

FIG. 11A is a plot of the 66 mRNA biomarkers against the AUC for differentiating between mild sepsis and severe sepsis with all individual biomarkers selected having an AUC greater than 0.7;

FIG. 11B is a box and whisker plot showing the best mRNA biomarker for separating mild sepsis and severe sepsis;

FIG. 11C is a plot of the AUC for the diagnostic ability of 1000 derived biomarkers in separating mild sepsis and severe sepsis with all derived biomarkers have an AUC of at least 0.87;

FIG. 11D is a box and whisker plot of the best performing derived biomarker, based on AUC, for separating mild sepsis and severe sepsis;

FIGS. 11E and 11F are plots showing the correlation to each other of the biomarkers in each group;

FIGS. 11G and 11H are plots demonstrating the AUC of the biomarkers in each group;

FIG. 11I is a box and whisker plot showing that when biomarkers are derived from group 1 and group 2 that a greater overall AUC is obtained;

FIG. 12A is a plot of the 48 mRNA biomarkers against the AUC for differentiating between mild sepsis and septic shock (also referred to herein as “infection-positive SIRS-shock” (ipSIRS-shock)) with all individual biomarkers having an AUC greater than 0.7;

FIG. 12B is a box and whisker plot showing the best mRNA biomarker for separating mild sepsis and septic shock;

FIG. 12C is a plot of the AUC for the diagnostic ability of 1000 derived biomarkers in separating mild sepsis and septic shock with all derived biomarkers having an AUC of at least 0.793;

FIG. 12D is a box and whisker plot of the best performing derived biomarker, based on AUC, for separating mild sepsis and septic shock;

FIGS. 12E and 12F are plots showing the correlation to each other of the biomarkers in each group;

FIGS. 12G and 12H are plots demonstrating the AUC of the biomarkers in each group;

FIG. 12I is a box and whisker plot showing that when biomarkers are derived from group 1 and group 2 that a greater overall AUC is obtained;

FIG. 13A is a plot of the 61 mRNA biomarkers against the AUC for differentiating between severe sepsis and septic shock with all individual biomarkers selected having an AUC greater than 0.7;

FIG. 13B is a box and whisker plot showing the best mRNA biomarker for separating severe sepsis and septic shock;

FIG. 13C is a plot of the AUC for the diagnostic ability of 1000 derived biomarkers in separating severe sepsis and septic shock with all derived biomarkers have an AUC of at least 0.821;

FIG. 13D is a box and whisker plot of the best performing derived biomarker, based on AUC, for separating severe sepsis and septic shock;

FIGS. 13E and 13F are plots showing the correlation to each other of the biomarkers in each group;

FIGS. 13G and 13H are plots demonstrating the AUC of the biomarkers in each group;

FIG. 13I is a box and whisker plot showing that when biomarkers are derived from group 1 and group 2 that a greater overall AUC is obtained;

FIG. 14 is a user interface illustrating an example of a thermal cycler protocol;

FIG. 15 is a diagram of an example of a report; and,

FIGS. 16A to 16L are box and whisker plots for the top twelve biomarker combinations for distinguishing between healthy condition and PS;

FIGS. 17A to 17L are box and whisker plots for the top twelve biomarker combinations for distinguishing between healthy condition and sepsis;

FIGS. 18A to 18L are box and whisker plots for the top twelve biomarker combinations for distinguishing between PS and sepsis;

FIGS. 19A to 19L are box and whisker plots for the top twelve biomarker combinations for distinguishing between sepsis and severe sepsis;

FIGS. 20A to 20L are box and whisker plots for the top twelve biomarker combinations for distinguishing between severe sepsis and septic shock;

FIGS. 21A to 21L are box and whisker plots for the top twelve biomarker combinations for distinguishing between sepsis and septic shock;

FIG. 22 is a graph of the effect on AUC of adding biomarkers to a biomarker signature;

FIG. 23 is a graph showing an example of the ability of the biomarker signature to distinguish between PS and sepsis for two patient populations;

FIG. 24 is a flowchart of an example of a method for determining indicator references;

FIGS. 25A and 25B are a flowchart of an example of a method for validating an indicator derived from biomarker measurements;

FIG. 26 is an example showing the comparison of an indicator value to an indicator reference; and,

FIGS. 27A and 27B are example representation of indicator values.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

An example of a method for determining an indicator for use in diagnosing the presence, absence or degree of at least one condition in or of a biological subject, or in monitoring the progression of at least one condition in or of the subject, or in prognosing at least one condition in or of the subject, will now be described with reference to FIG. 1A.

For the purpose of explanation, a number of different terms will be used. For example, the term “biomarker” refers to a measurable parameter, or combination of parameters, that can be used as an indicator of a biological state and includes, without limitation, proteins, nucleic acids, carbohydrates, lipids, metabolites, gases, steroids, ions, nutrients, toxins, cells, pathogenic organisms, non-pathogenic organisms, organic compounds and inorganic compounds. Biomarkers also encompass non-blood-borne factors, non-analyte physiological markers of health status, or other factors or biomarkers not measured from samples (e.g., biological samples such as bodily fluids), such as “clinical” or “phenotypic” parameters, including, without limitation, age, ethnicity, gender, species, breed, genetic information, white blood cell count, diastolic blood pressure and systolic blood pressure, bone density, height, weight, waist and hip circumference, body-mass index, as well as others such as Type I or Type II diabetes mellitus or gestational diabetes mellitus (collectively referred to here as diabetes), resting heart rate, homeostatic model assessment (HOMA), HOMA insulin resistance (HOMA-IR), intravenous glucose tolerance (SI(IVGT)), resting heart rate, β cell function, macrovascular function, microvascular function, atherogenic index, low-density lipoprotein/high-density lipoprotein ratio, intima-media thickness, body temperature, sequential organ failure assessment (SOFA) and the like. The “biomarkers” could also include “immune response biomarkers”, which will be described in more detail below.

The term “biomarker value” refers to a value measured or derived for at least one corresponding biomarker of the biological subject and which is typically at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject. Thus, the biomarker values could be measured biomarker values, which are values of biomarkers measured for the subject, or alternatively could be derived biomarker values, which are values that have been derived from one or more measured biomarker values, for example by applying a function to the one or more measured biomarker values.

Biomarker values can be of any appropriate form depending on the manner in which the values are determined. For example, the biomarker values could be determined using high-throughput technologies such as mass spectrometry, sequencing platforms, array and hybridization platforms, immunoassays, flow cytometry, or any combination of such technologies and in one preferred example, the biomarker values relate to a level of activity or abundance of an expression product or other measurable molecule, quantified using a technique such as PCR, sequencing or the like. In this case, the biomarker values can be in the form of amplification amounts, or cycle times, which are a logarithmic representation of the concentration of the biomarker within a sample, as will be appreciated by persons skilled in the art and as will be described in more detail below.

The term “reference biomarkers” is used to refer to biomarkers whose activity has been quantified for a sample population of one or more individuals having one or more conditions, stages of one or more conditions, subtypes of one or more conditions or different prognoses. The term “reference data” refers to data measured for one or more individuals in a sample population, and may include quantification of the level or activity of the biomarkers measured for each individual, information regarding any conditions of the individuals, and optionally any other information of interest.

The term “candidate biomarkers” refers to a subset of the reference biomarkers that have been identified as being potentially useful in distinguishing between different groups of individuals, such as individuals suffering from different conditions, or having different stages or prognoses. The number of candidate biomarkers will vary, but is typically about 200.

The term “signature biomarkers” is used to refer to a subset of the candidate biomarkers that have been identified for use in a biomarker signature that can be used in performing a clinical assessment, such as to rule in or rule out a specific condition, different stages or severity of conditions, subtypes of different conditions or different prognoses. The number of signature biomarkers will vary, but is typically of the order of 10 or less.

The term “biomarker signature” means a combination of at least two biomarker values corresponding to values of biomarkers that can be measured for or derived from one or more biological subjects, which combination is characteristic for a discrete condition, stage of condition, subtype of condition or a prognosis for a discrete condition, stage of condition, subtype of condition.

The terms “biological subject”, “subject”, “individual” and “patient” are used interchangeably herein to refer to an animal subject, particularly a vertebrate subject, and even more particularly a mammalian subject. Suitable vertebrate animals that fall within the scope of the invention include, but are not restricted to, any member of the phylum Chordata, subphylum vertebrata including primates, rodents (e.g., mice rats, guinea pigs), lagomorphs (e.g., rabbits, hares), bovines (e.g., cattle), ovines (e.g., sheep), caprines (e.g., goats), porcines (e.g., pigs), equines (e.g., horses), canines (e.g., dogs), felines (e.g., cats), avians (e.g., chickens, turkeys, ducks, geese, companion birds such as canaries, budgerigars etc.), marine mammals (e.g., dolphins, whales), reptiles (snakes, frogs, lizards, etc.), and fish. A preferred subject is a primate (e.g., a human, ape, monkey, chimpanzee).

As used herein, the term SIRS (“systemic inflammatory response syndrome”) refers to a clinical response arising from a non-specific insult with two or more of the following measurable clinical characteristics; a body temperature greater than 38° C. or less than 36° C., a heart rate greater than 90 beats per minute, a respiratory rate greater than 20 per minute, a white blood cell count (total leukocytes) greater than 12,000 per mm³ or less than 4,000 per mm³, or a band neutrophil percentage greater than 10%. From an immunological perspective, it may be seen as representing a systemic response to insult (e.g., major surgery) or systemic inflammation. As used herein, “inSIRS” (which includes within its scope “post-surgical” (PS) inflammation) includes the clinical response noted above but in the absence of a systemic infectious process. By contrast, “ipSIRS” (also referred to herein as “sepsis”) includes the clinical response noted above but in the presence of a presumed or confirmed infection. Confirmation of infection can be determined using microbiological culture or isolation of the infectious agent. From an immunological perspective, ipSIRS may be seen as a systemic response to microorganisms, whether it is a local, peripheral or systemic infection.

As used herein, the term “degree” of a condition refers to the seriousness, severity, stage or state of a condition. For example, a condition may be characterized as mild, moderate or severe. A person of skill in the art would be able to determine or assess the degree of a particular condition. For example, the degree of a condition may be determined by comparing the likelihood or length of survival of a subject having a condition with the likelihood or length of survival in other subjects having the same condition. In other embodiments, the degree of a condition may be determined by comparing the clinical signs of a subject having a condition with the degree of the clinical signs in other subjects having the same condition.

It will be appreciated that the above described terms and associated definitions are used for the purpose of explanation only and are not intended to be limiting.

In this example, the method includes determining a plurality of biomarker values at step 100, each biomarker value being indicative of a value measured or derived for at least one biomarker of the biological subject.

The biomarker values and biomarkers corresponding to the biomarker values can be of any appropriate form and in particular can relate to any attribute of a subject for which a value can be quantified. This technique is particularly suited to high-throughput technologies such as mass spectrometry, sequencing platforms, array and hybridization platforms, or any combination of such technologies and in one preferred example, the biomarker values relate to a level of activity or abundance of an expression product or other measurable molecule.

The biomarker values could be measured biomarker values, which are values of biomarkers measured for the subject, or alternatively could be derived biomarker values, which are values that have been derived from one or more measured biomarker values, for example by applying a function to the one or more measured biomarker values. As used herein, biomarkers to which a function has been applied are referred to as “derived markers”.

The biomarker values may be determined in any one of a number of ways. In one example, the process of determining the biomarker values can include measuring the biomarker values, for example by performing tests on the biological subject. More typically however, the step of determining the biomarker values includes having an electronic processing device receive or otherwise obtain biomarker values that have been previously measured or derived. This could include for example, retrieving the biomarker values from a data store such as a remote database, obtaining biomarker values that have been manually input, using an input device, or the like.

At step 110, the indicator is determined using a combination of the plurality of biomarker values, the indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition.

The biomarker values can be combined in any one of a number of ways and this can include for example adding, multiplying, subtracting, or dividing biomarker values to determine an indicator value. This step is performed so that multiple biomarker values can be combined into a single indicator value, providing a more useful and straightforward mechanism for allowing the indicator to be interpreted and hence used in diagnosing the presence, absence or degree of the at least one condition in the subject, or prognosing the at least one condition in the subject.

Assuming the method is performed using an electronic processing device, at step 120 an indication of the indicator is optionally displayed or otherwise provided to the user. In this regard, the indication could be a graphical or alphanumeric representation of an indicator value. Alternatively however, the indication could be the result of a comparison of the indicator value to predefined thresholds or ranges, or alternatively could be an indication of the presence, absence, degree or prognosis for at least one condition, derived using the indicator.

In order to ensure an effective diagnosis or prognosis can be determined, at least two of the biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9. This requirement means that the two biomarkers are not entirely correlated in respect of each other when considered in the context of the condition(s) being diagnosed or prognosed. In other words, at least two of the biomarkers in the combination respond differently as the condition changes, which adds significantly to their ability when combined to discriminate between at least two conditions, to diagnose the presence, absence or degree of at least one condition, and/or to provide a prognosis of at least condition in or of the biological subject. As used herein, “and/or” refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (or).

Typically, the requirement that biomarkers have a low mutual correlation means that the biomarkers may relate to different biological attributes or domains such as but not limited to different molecular functions, different biological processes and different cellular components. Illustrative examples of molecular function include addition of or removal of one of more of the following moieties to or from a protein, polypeptide, peptide, nucleic acid (e.g., DNA, RNA): linear, branched, saturated or unsaturated alkyl (e.g., C₁-C₂₄ alkyl); phosphate; ubiquitin; acyl; fatty acid, lipid, phospholipid; nucleotide base; hydroxyl and the like. Molecular functions also include signaling pathways, including without limitation, receptor signaling pathways and nuclear signaling pathways. Non-limiting examples of molecular functions also include cleavage of a nucleic acid, peptide, polypeptide or protein at one or more sites; polymerization of a nucleic acid, peptide, polypeptide or protein; translocation through a cell membrane (e.g., outer cell membrane; nuclear membrane); translocation into or out of a cell organelle (e.g., Golgi apparatus, lysosome, endoplasmic reticulum, nucleus, mitochondria); receptor binding, receptor signaling, membrane channel binding, membrane channel influx or efflux; and the like.

Illustrative examples of biological processes include: stages of the cell cycle such as meiosis, mitosis, cell division, prophase, metaphase, anaphase, telophase and interphase, stages of cell differentiation; apoptosis; necrosis; chemotaxis; immune responses including adaptive and innate immune responses, pro-inflammatory immune responses, autoimmune responses, tolerogenic responses and the like. Other illustrative examples of biological processes include generating or breaking down adenosine triphosphate (ATP), saccharides, polysaccharides, fatty acids, lipids, phospholipids, sphingolipids, glycolipids, cholesterol, nucleotides, nucleic acids, membranes (e.g., cell plasma membrane, nuclear membrane), amino acids, peptides, polypeptides, proteins and the like.

Representative examples of cellular components include organelles, membranes, as for example noted above, and others.

It will be appreciated that the use of biomarkers that have different biological attributes or domains provides further information than if the biomarkers were related to the same or common biological attributes or domains.

In this regard, it will be appreciated if the at least two biomarkers are highly correlated to each other, the use of both biomarkers would add little diagnostic/prognostic improvement compared to the use of a single one of the biomarkers. Accordingly, the method uses biomarkers that are not well correlated with each other, thereby ensuring that the inclusion of each biomarker in the method adds significantly to the discriminative ability of the indicator.

Despite this, in order to ensure that the indicator can accurately be used in performing the discrimination between at least two conditions or the diagnosis of the presence, absence or degree of at least one condition or the prognosis of at least one condition, the indicator has a performance value that is greater than or equal to a performance threshold. The performance threshold may be of any suitable form but is to be typically indicative of an explained variance of at least 0.3, or an equivalent value of another performance measure.

It has been found that utilizing a combination of biomarkers that have a mutual correlation between ±0.9 and using these in a combination that provides an explained variance of at least 0.3, this allows an indicator to be defined that is suitable for ensuring that an accurate discrimination, diagnosis or prognosis can be obtained whilst minimizing the number of biomarkers that are required.

It will be appreciated that in this context, the biomarkers used within the above-described method can define a biomarker signature for the at least one condition, which includes a minimal number of biomarkers, whilst maintaining sufficient performance to allow the biomarker signature to be used in making a clinically relevant diagnosis, prognosis, or differentiation. Minimizing the number of biomarkers used minimizes the costs associated with performing diagnostic or prognostic tests and in the case of nucleic acid expression products, allows the test to be performed utilizing relatively straightforward techniques such as nucleic acid array, and polymerase chain reaction (PCR) processes, or the like, allowing the test to be performed rapidly in a clinical environment.

Furthermore, producing a single indicator value allows the results of the test to be easily interpreted by a clinician or other medical practitioner, so that test can be used for reliable diagnosis in a clinical environment.

An example of the process for generating a suitable biomarker signature for use in the method of FIG. 1A will now be described with reference to FIG. 1B.

In particular, it is typical to generate a biomarker signature by analyzing a large number of biomarkers and then selecting a combination of biomarkers that meet the above described criteria.

In this example, at step 150 the process includes ranking a number of candidate biomarkers in accordance with the ability of each biomarker to distinguish between the presence, absence, degree or prognosis of at least one condition in a biological subject.

The candidate biomarkers can be obtained in any appropriate matter, but typically this would involve acquiring reference data including reference biomarker values relating to a number of reference biomarkers that have been measured or derived for one or more reference individuals having different presences, absences, degrees or prognoses of the one or more conditions of interest. Thus, it will be appreciated that the candidate biomarkers can include measured and/or derived biomarkers, as will be described in more detail below.

The reference data typically includes measurements of a plurality of reference biomarkers, the measurements including information regarding the activity, such as the level, abundance or functional activity, of any expression product or measurable molecule, as will be described in more detail below. The reference data may also include information such as clinical data regarding one or more conditions suffered by each individual. This can include information regarding a presence, absence, degree or progression of a condition, phenotypic information, such as details of phenotypic traits, genetic or genetically regulated information, amino acid or nucleotide related genomics information, results of other tests including imaging, biochemical and hematological assays, other physiological scores such as a SOFA (Sequential Organ Failure Assessment) score, or the like and this is not intended to be limiting, as will be apparent from the description below.

The candidate biomarkers can include some or all of the reference biomarkers, depending on the preferred implementation. Thus, for example, reference biomarker values could be analyzed to determine correlations between the reference biomarkers and the at least one condition, with the reference biomarkers being coarsely filtered to remove those with a low correlation, for example with a correlation with the condition that is below 0.3.

At step 160 at least two candidate biomarkers are selected based on the ranking and a mutual correlation. In particular, at least two candidate biomarkers are selected which have a mutual correlation within a mutual correlation range of ±0.9. Thus, this process excludes any biomarkers which are highly mutually correlated, when considered in the context of the one or more conditions, and which would not therefore add significantly to the ability to discriminate between the presence, absence, degree or prognosis of at least one condition.

At step 170 a performance value of a combination of the selected candidate biomarkers is determined. As mentioned above the combination may be any combination of the candidate biomarker values, such as addition, subtraction, multiplication, or division of the candidate biomarker values, and this will not therefore be described in any further detail.

At step 180 it is determined if the performance value of the combination exceeds a performance threshold, the performance threshold being equivalent to an explained variance of at least 0.3. If so, the combination of the candidate biomarkers can be used to define a biomarker signature. Otherwise, the previous steps can be repeated, for example by determining alternative combinations of the candidate biomarkers, selecting different candidate biomarkers, or adding additional candidate biomarkers as will be described in more detail below. In this regard, it will be appreciated that other measures could be used, and reference to an explained variance of at least 0.3 is intended to be a particular example for illustrative purposes.

Accordingly, the above described method can be utilized to select a combination of candidate biomarkers that are suitable for use as signature biomarkers in a biomarker signature for diagnosing the presence, absence, degree or prognosis of at least one condition in a biological subject, for example using the method of FIG. 1A above.

In one example, this is achieved by ensuring that at least two of the biomarkers used are not highly mutually correlated, thereby ensuring that each of these biomarkers contributes to the performance of the resulting signature.

A number of further features will now be described.

In one example, the method includes determining a plurality of measured biomarker values, each measured biomarker value being a measured value of a corresponding biomarker of the biological subject and applying a function to at least one of the measured biomarker values to determine at least one derived biomarker value, the at least one derived biomarker value being indicative of a value of a corresponding derived biomarker.

The function used will therefore vary depending on the preferred implementation. In one example, the function includes at least one of multiplying two biomarker values; dividing two biomarker values; adding two biomarker values; subtracting two biomarker values; a weighted sum of at least two biomarker values; a log sum of at least two biomarker values; and, a sigmoidal function of at least two biomarker values.

More typically the function is division of two biomarker values, so that the derived biomarker value corresponds to a ratio of two measured biomarker values. There are a number of reasons why the ratio might be preferred. For example, use of a ratio is self-normalizing, meaning variations in measuring techniques will automatically be accommodated. For example, if the input concentration of a sample is doubled, the relative proportions of biomarkers will remain the same. As a result, the type of function therefore has a stable profile over a range of input concentrations, which is important because input concentration is a known variable for expression data. Additionally, many biomarkers are nodes on biochemical pathways, so the ratio of biomarkers gives information about the relative activation of one biological pathway to another, which is a natural representation of biological change within a system. Finally, ratios are typically easily interpreted.

The method typically includes combining at least two biomarker values to determine an indicator value representing the indicator. This is usually achieved by combining at least two biomarker values using a combining function, such as: an additive model; a linear model; a support vector machine; a neural network model; a random forest model; a regression model; a genetic algorithm; an annealing algorithm; and a weighted sum.

In one example, at least one of the at least two biomarkers is a derived biomarker and in a preferred example, the combining function is addition of derived biomarker values that are ratios, in which case the method includes determining first and second derived biomarker values from ratios of first and second and third and fourth measured biomarker values, and then adding the first and second derived biomarker values to generate an indicator value.

In one example, the method includes determining an indicator value, comparing the indicator value to at least one indicator value range, and using a result of the comparison in diagnosing the presence, absence, degree or prognosis of at least one condition.

The above-described process is typically performed using an electronic processing device, forming part of a processing system such as a computer system or the like. In this case, the method typically involves having the electronic processing device receive a plurality of measured biomarker values, apply a function to at least one of the measured biomarker values to determine the at least one derived biomarker value and combining at least one derived biomarker value and at least one other biomarker value to determine an indicator value.

The electronic processing device can then generate a representation in accordance with the at least one indicator value, for example by displaying a numerical indication of the indicator value. More typically however the electronic processing device compares the indicator value to at least one indicator value range and displays a result of the comparison. This can be used to compare the indicator to defined ranges representing specific stages, progressions or prognoses of one or more conditions, allowing an indication of the respective stage, progression or prognosis to be displayed.

The mutual correlation range is typically at least one of: ±0.8; ±0.7; ±0.6; ±0.5; ±0.4; ±0.3; ±0.2; and ±0.1. In this regard, it will be appreciated that the smaller the mutual correlation range used, the less correlated the biomarkers will be and hence the more useful these will be in discriminating between the specific stages, progressions or prognoses of one or more conditions.

It is also typical for the biomarkers used to have at least a minimum correlation with the condition. In one example, each biomarker has a condition correlation with the presence, absence, degree or prognosis of the at least one condition that lies outside a condition correlation range, the condition correlation range being between ±0.3. However, it will be appreciated that the greater the range, the greater the correlation between the biomarker and condition, and hence the more use this will be in performing a diagnosis. Accordingly, the condition correlation is more typically one of: ±0.9; ±0.8; ±0.7; ±0.6; ±0.5; and ±0.4.

Furthermore, it will be appreciated that the greater the performance of the indicator, the greater use the indicator will be in performing the diagnosis. Accordingly, the performance threshold is typically indicative of an explained variance of at least one of: 0.4; 0.5; 0.6; 0.7; 0.8; and 0.9.

As described above, the biomarker value can be of any suitable form. However, the technique is particularly suited to biomarker values indicative of a level or abundance of a molecule selected from one or more of: a nucleic acid molecule; a proteinaceous molecule; an amino acid; a carbohydrate; a lipid; a steroid; an inorganic molecule; an ion; a drug; a chemical; a metabolite; a toxin; a nutrient; a gas; a cell; a pathogenic organism; and a non-pathogenic organism.

When determining biomarkers for use in a biomarker signature, the method typically includes selecting a combining function, determining if a performance value of a combination of the at least two candidate biomarkers determined by the combining function is greater than or equal to a performance threshold and if the performance value is not greater than or equal to a performance threshold, repeating these steps for successive different combining functions. Thus, this allows a number of different combining functions to be tried successively, allowing the best combining function to be identified.

The method typically further includes selecting two candidate biomarkers, determining if a performance value of a combination of the two candidate biomarkers is greater than or equal to a performance threshold and if not, combining the selected candidate biomarkers with at least one additional candidate biomarker before repeating the steps with at least one additional candidate biomarker. This allows a larger number of biomarkers to be used in the event two biomarkers are insufficient, and this can be repeated, with increasing numbers of candidate biomarkers used in combination and compared to the performance threshold until the required performance is reached, or up until a defined number limit of candidate biomarkers is reached.

To ensure the required mutual correlation is met when selecting the candidate biomarkers, the method typically includes selecting a highest and a next highest ranked candidate biomarker, for the selected candidate biomarkers, determining if the mutual correlation for the candidate biomarkers within the mutual correlation range and if not repeating these steps until two candidate biomarkers are selected having a mutual correlation within the mutual correlation range. Alternatively however this can be achieved by defining at least two groups of candidate biomarkers, candidate biomarkers in different groups having a mutual correlation within the mutual correlation range, ranking the candidate biomarkers in each group and selecting candidate biomarkers from the different groups.

In general, the candidate biomarkers are determined by using reference data for at least one individual to define a number of groups indicative of the presence, absence, degree or prognosis of the at least one condition and then using at least one analysis technique to identify a number of candidate biomarkers that are potentially useful for distinguishing the groups. The groups can also be used to establish a range of at least two reference biomarker values to determine an indicator value range for the group.

In one example, reference values measured for reference biomarkers for the at least one individual can then be used to identify the candidate biomarkers, for example by filtering reference biomarkers to determine candidate biomarkers based on a correlation of each biomarker with the condition.

In one example, the process is performed by one or more processing systems operating as part of a distributed architecture, an example of which will now be described with reference to FIG. 2.

In this example, the arrangement includes a number of processing systems 201 and computer systems 203 interconnected via one or more communications networks, such as the Internet 202, and/or a number of local area networks (LANs) 204. It will be appreciated that the configuration of the networks 202, 204 is for the purpose of example only, and in practice the processing and computer systems 201, 203 can communicate via any appropriate mechanism, such as via wired or wireless connections, including, but not limited to mobile networks, private networks, such as an 802.11 networks, the Internet, LANs, WANs, or the like, as well as via direct or point-to-point connections, such as Bluetooth, or the like.

The use of separate terms “processing system” and “computer system” is for illustrative purposes and to enable distinction between different devices, optionally having different functionality. For example, the processing and computer systems 201, 203 could represent servers and clients respectively, as will become apparent from the following description. However, this is not intended to be limiting and in practice any suitable computer network architecture can be used.

An example of a suitable processing system 201 is shown in FIG. 3. In this example, the processing system 201 includes an electronic processing device, such as at least one microprocessor 300, a memory 301, an optional input/output device 302, such as a keyboard and/or display, and an external interface 303, interconnected via a bus 304 as shown. In this example the external interface 303 can be utilized for connecting the processing system 201 to peripheral devices, such as the communications networks 202, 204, databases 211, other storage devices, or the like. Although a single external interface 303 is shown, this is for the purpose of example only, and in practice multiple interfaces using various methods (e.g., Ethernet, serial, USB, wireless or the like) may be provided.

In use, the microprocessor 300 executes instructions in the form of applications software stored in the memory 301 to perform required processes, such as communicating with other processing or computer systems 201, 203. Thus, actions performed by a processing system 201 are performed by the processor 300 in accordance with instructions stored as applications software in the memory 301 and/or input commands received via the I/O device 302, or commands received from other processing or computer systems 201, 203. The applications software may include one or more software modules, and may be executed in a suitable execution environment, such as an operating system environment, or the like.

Accordingly, it will be appreciated that the processing systems 201 may be formed from any suitable processing system, such as a suitably programmed computer system, PC, web server, network server, or the like. In one particular example, the processing systems 201 are standard processing system such as a 32-bit or 64-bit Intel Architecture based processing system, which executes software applications stored on non-volatile (e.g., hard disk) storage, although this is not essential. However, it will also be understood that the processing system could be or could include any electronic processing device such as a microprocessor, microchip processor, logic gate configuration, firmware optionally associated with implementing logic such as an FPGA (Field Programmable Gate Array), or any other electronic device, system or arrangement.

As shown in FIG. 4, in one example, the computer systems 203 include an electronic processing device, such as at least one microprocessor 400, a memory 401, an input/output device 402, such as a keyboard and/or display, and an external interface 403, interconnected via a bus 404 as shown. In this example the external interface 403 can be utilized for connecting the computer system 203 to peripheral devices, such as the communications networks 202, 204, databases, other storage devices, or the like. Although a single external interface 403 is shown, this is for the purpose of example only, and in practice multiple interfaces using various methods (e.g., Ethernet, serial, USB, wireless or the like) may be provided.

In use, the microprocessor 400 executes instructions in the form of applications software stored in the memory 401 to perform required processes, for example to allow communication with other processing or computer systems 201, 203. Thus, actions performed by a processing system 203 are performed by the processor 400 in accordance with instructions stored as applications software in the memory 401 and/or input commands received from a user via the I/O device 402. The applications software may include one or more software modules, and may be executed in a suitable execution environment, such as an operating system environment, or the like.

Accordingly, it will be appreciated that the computer systems 203 may be formed from any suitable processing system, such as a suitably programmed PC, Internet terminal, lap-top, hand-held PC, smart phone, PDA, tablet, or the like. Thus, in one example, the processing system 300 is a standard processing system such as a 32-bit or 64-bit Intel Architecture based processing system, which executes software applications stored on non-volatile (e.g., hard disk) storage, although this is not essential. However, it will also be understood that the processing systems 203 can be any electronic processing device such as a microprocessor, microchip processor, logic gate configuration, firmware optionally associated with implementing logic such as an FPGA (Field Programmable Gate Array), or any other electronic device, system or arrangement.

It will also be noted that whilst the processing and computer systems 201, 203 are shown as single entities, it will be appreciated that this is not essential, and instead one or more of the processing and/or computer systems 201, 203 can be distributed over geographically separate locations, for example by using processing systems provided as part of a cloud based environment.

Examples of the above-described method(s) will now be described in further detail. For the purpose of these examples, it is assumed that the process is performed by one or more of the processing systems 201, acting as diagnostic servers. Interaction by a user is via a user computer system 203, which is used to allow a user to submit raw data, for example obtained from measurements on a subject, with the processing system 201 generating the indicator, allowing this to be displayed on the computer system 203.

However, it will be appreciated that the above-described configuration assumed for the purpose of the following examples is not essential, and numerous other configurations may be used. It will also be appreciated that the partitioning of functionality between the processing and computer systems 201, 203 may vary, depending on the particular implementation.

An example of a specific method for identifying biomarkers for using a biomarker signature will now be described with reference to FIG. 5.

In this example, at step 500 reference data is obtained from at least one individual. The reference data is typically in the form of measured biomarker values obtained for at least one individual for different stages of the at least one condition.

The reference data may be acquired in any appropriate manner but typically this involves obtaining gene expression product data from a plurality of individuals, selected to include individuals diagnosed with one or more conditions of interest, as well as healthy individuals. Detection of either types of gene expression in use of any of the methods described herein is encompassed by the present invention. The terms “expression” or “gene expression” refer to production of RNA only or production of RNA and translation of RNA into proteins or polypeptides. Thus, the term “expression products” encompass (i) polynucleotides including RNA transcripts and corresponding nucleic acids including complementary cDNA copies of RNA transcripts, and (ii) polypeptides encoded by RNA transcripts. The conditions are typically medical, veterinary or other health status conditions and may include any illness, disease, stages of disease, disease subtypes, severities of disease, diseases of varying prognoses or the like. The terms “healthy individual”, “healthy subject” and the like are used herein to refer to a subject, in particular a mammal, having no diagnosed disease, disorder, infirmity, or ailment. The condition of such an individual or subject is referred to herein as a “healthy condition” such that in one example a condition can include healthy. In specific embodiments, a healthy subject lacks SIRS (e.g., inSIRS or ipSIRS).

In order to achieve this, gene expression product data are collected, for example by obtaining a biological sample, such as a peripheral blood sample, and then performing a quantification process, such as a nucleic acid amplification process, including PCR (Polymerase Chain Reaction) or the like, in order to assess the activity, and in particular, level or abundance of a number of reference biomarkers. Quantified values indicative of the relative activity are then stored as part of the reference data.

Example reference biomarkers could include expression products such as nucleic acid or proteinaceous molecules, as well as other molecules relevant in making a clinical assessment. The number of biomarkers measured for use as reference biomarkers will vary depending upon the preferred implementation, but typically include a large number such as, 1000, 5000, 10000 or above, although this is not intended to be limiting.

The individuals also typically undergo a clinical assessment allowing any conditions to be clinically identified, and with an indication of any assessment or condition forming part of the reference data. Whilst any conditions can be assessed, in one example the process is utilized specifically to identify conditions such as SIRS (Systemic Inflammatory Response Syndrome) (M S Rangel-Frausto, D Pittet, M Costigan, T Hwang, C S Davis, and R P Wenzel, “The Natural History of the Systemic Inflammatory Response Syndrome (SIRS). a Prospective Study.”, JAMA: the Journal of the American Medical Association 273, no. 2 (Jan. 11, 1995): 117-123.). SIRS is an overwhelming whole body reaction that may have an infectious or non-infectious etiology, whereas sepsis is SIRS that occurs during infection. Both are defined by a number of non-specific host response parameters including changes in heart and respiratory rate, body temperature and white cell counts (Mitchell M Levy et al., “2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference”, Critical Care Medicine 31, no. 4 (April 2003): 1250-1256.; K Reinhart, M Bauer, N C Riedemann, and C S Hartog, “New Approaches to Sepsis: Molecular Diagnostics and Biomarkers”, Clinical Microbiology Reviews 25, no. 4 (Oct. 3, 2012): 609-634) To differentiate these conditions they are referred herein to as SIRS (both conditions), infection-negative SIRS (SIRS without infection, hereafter referred to as “inSIRS”) and infection-positive SIRS (sepsis, SIRS with a known or suspected infection, hereafter referred to as “ipSIRS”) The causes of SIRS are multiple and varied and can include, but are not limited to, trauma, burns, pancreatitis, endotoxemia, surgery, adverse drug reactions, and infections (local and systemic). It will be appreciated from the following, however, that this can be applied to a range of different conditions, and reference to SIRS or sepsis is not intended to be limiting.

Additionally, the reference data may include additional biomarkers such as one or more phenotypic or clinical parameters of the individuals and/or their relatives. Phenotypic parameters can include information such as the gender, ethnicity, age, hair color, eye color, height, weight, waist and hip circumference, or the like. Also, in the case of the technology being applied to individuals other than humans, this can also include information such as designation of a species, breed or the like. Clinical traits may include genetic information, white blood cell count, diastolic blood pressure and systolic blood pressure, bone density, body-mass index, diabetes, resting heart rate, HOMA, HOMA-IR, IVGT, resting heart rate, β cell function, macrovascular function, microvascular function, atherogenic index, low-density lipoprotein/high-density lipoprotein ratio, intima-media thickness, body temperature, SOFA and the like.

Accordingly, in one example the reference data can include for each of the reference individuals information relating to at least one and desirably to a plurality of reference biomarkers and a presence, absence, degree or progression of a condition.

The reference data may be collected from individuals presenting at a medical center with clinical signs relating to relevant any conditions of interest, and may involve follow-on consultations in order to confirm clinical assessments, as well as to identify changes in biomarkers, and/or clinical signs, and/or severity of clinical signs, over a period of time. In this latter case, the reference data can include time series data indicative of the progression of a condition, and/or the activity of the reference biomarkers, so that the reference data for an individual can be used to determine if the condition of the individual is improving, worsening or static. It will also be appreciated that the reference biomarkers are preferably substantially similar for the individuals within the sample population, so that comparisons of measured activities between individuals can be made.

This reference data could also be collected from a single individual over time, for example as a condition within the individual progresses, although more typically it would be obtained from multiple individuals each of which has a different stage of the one or more conditions of interest.

It will be appreciated that once collected, the reference data can be stored in the database 211 allowing this to be subsequently retrieved by the processing system 201 for subsequent analysis. The processing system 201 also typically stores an indication of an identity of each of the reference biomarkers.

In one example, the measurements are received as raw data, which then undergoes preliminary processing. Such raw data corresponds to information that has come from a source without modification, such as outputs from instruments such as PCR machines, array (e.g., microarray) scanners, sequencing machines, clinical notes or any other biochemical, biological, observational data, or the like. This step can be used to convert the raw data into a format that is better suited to analysis. In one example this is performed in order to normalize the raw data and thereby assist in ensuring the biomarker values demonstrate consistency even when measured using different techniques, different equipment, or the like. Thus, the goal of normalization is to remove the variation within the samples that is not directly attributable to the specific analysis under consideration. For example, to remove variances caused by differences in sample processing at different sites. Classic examples of normalization include z-score transformation for generic data, or popular domain specific normalizations, such as RMA normalization for microarrays.

However, it will also be appreciated that in some applications, such as a single sample experiment run on a single data acquisition machine, this step may not strictly be necessary, in which case the function can be a Null function producing an output identical to the input.

In one example, the preferred approach is a paired function approach over log normalized data. Log normalization is a standard data transformation on microarray data, because the data follow a log-normal distribution when coming off the machine. Applying a log transform turns the data into process-friendly normal data.

At step 505 different groups are defined using the reference data.

Prior to this occurring, the processing system 201 optionally removes a validation subgroup of individuals from the reference data to allow the processing system 201 to determine the candidate biomarkers using the reference data without the validation subgroup so that the validation subgroup can be subsequently used to validate the candidate biomarkers or signatures including a number of the candidate biomarkers. Thus, data from the validation subgroup is used to validate the efficacy of the candidate or signature biomarkers in identifying the presence, absence, degree, stage, severity, prognosis or progression of any one or more of the conditions to ensure the potential or signature biomarkers are effective.

In one example, this is achieved by having the processing system 201 flag individuals within the validation subgroup or alternatively store these in either an alternative location within the database 211 or an alternative database to the reference data. The validation subgroup of individuals is typically selected randomly and may optionally be selected to include individuals having different phenotypic traits. When a validation subgroup of individuals is removed, the remaining individuals will simply be referred to as reference data for ease throughout the remaining description.

The reference data (i.e., excluding the validation subgroup) are classified into groups. The groups may be defined in any appropriate manner and may be defined based on any one or more of an indication of a presence, absence, degree, stage, severity, prognosis or progression of a condition, other tests or assays, or measured biomarkers associated with the individuals.

For example, a first selection of groups may be to identify one or more groups of individuals suffering from SIRS, one or more groups of individuals suffering ipSIRS, one or more groups of individuals suffering inSIRS, and one or more groups of healthy individuals. Further groups may also be defined for individuals suffering from other conditions. The groups may include overlapping groups, so for example it may be desirable to define groups of healthy individuals and individuals having SIRS, with further being defined to distinguish inSIRS patients from ipSIRS patients, as well as different degree of inSIRS or ipSIRS, with these groups having SIRS in common, but each group of patients differing in whether a clinician has determined the presence of an infection or not. Additionally, further subdivision may be performed based on phenotypic traits, so groups could be defined based on gender, ethnicity or the like so that a plurality of groups of individuals suffering from a condition are defined, with each group relating to a different phenotypic trait.

It will also be appreciated, however, that identification of different groups can be performed in other manners, for example on the basis of particular activities of biomarkers within the biological samples of the reference individuals, and accordingly, reference to conditions is not intended to be limiting and other information may be used as required.

The manner in which classification into groups is performed may vary depending on the preferred implementation. In one example, this can be performed automatically by the processing system 201, for example, using unsupervised methods such as Principal Components Analysis (PCA), or supervised methods such as k-means or Self Organizing Map (SOM). Alternatively, this may be performed manually by an operator by allowing the operator to review reference data presented on a Graphical User Interface (GUI), and define respective groups using appropriate input commands.

At step 510 biomarkers are filtered based on their ability to distinguish between the groups. This process typically examines the activity of the reference biomarkers for individuals within and across the groups, to identify reference biomarkers whose activities differ between and hence can distinguish groups. A range of different analysis techniques can be utilized including, for example, regression or correlation analysis techniques. Examples of the techniques used can include established methods for parameterized model building such as Partial Least Squares, Random Forest or Support Vector Machines, usually coupled to a feature reduction technique for the selection of the specific subset of the biomarkers to be used in a signature.

Such techniques are known and described in a number of publications. For example, the use of Partial Least Squares is described in “Partial least squares: a versatile tool for the analysis of high-dimensional genomic data” by Boulesteix, Anne-Laure and Strimmer, Korbinian, from Briefings in Bioinformatics 2007 vol 8. no. 1, pg 32-44. Support Vector machines are described in “LIBSVM: a library for support vector machines” by Chang, C. C. and Lin, C. J. from ACM Transactions on Intelligent Systems and Technology (TIST), 2011 vol 2, no. 3, pg 27. Standard Random Forest in R language is described in “Classification and Regression by random Forest” by Liaw, A. and Wiener, M., in R news 2002, vol 2, no. 3, pg 18-22.

The analysis techniques are implemented by the processing system 201, using applications software, which allows the processing system 201 to perform multiple ones of the analysis techniques in sequence. This is advantageous as the different analysis techniques typically have different biases and can therefore be used to identify different potential biomarkers that can distinguish the groups, thereby reducing the risk of clinically relevant biomarkers being overlooked.

In one example, the process involves filtering out any biomarkers that demonstrate a correlation with the groups, and hence with the condition, that is below a certain correlation threshold, such as 0.3.

At step 515 derived biomarkers are generated from the filtered reference biomarkers using one or more functions. The nature of the derived biomarkers and the functions used will vary depending upon the preferred implementation. For example, functions can include division, subtraction, multiplication, addition of two markers, sigmoidal functions applied to the product of two biomarkers, negative logs of the division of two biomarkers, least-squares regression applied to two vectors of markers to produce a function (equation) output, concordance correlation coefficient of two vectors of categorical biomarkers, or the like.

In general, the function is selected based on a number of rules. These rules can include: utility, functions that provide the best results; interpretability, functions that can be understood in terms of biological function; output, functions that produce informative outputs; simplicity; performance assessment; least number of biomarkers for best performance; number of biomarkers at a statistical overfitting threshold or the like.

In one example, the preferred function is division, with the resulting biomarkers being different ratios. It will be appreciated that the division can be performed in multiple different ways, so that for three biomarkers, nine different derived biomarkers can be determined.

At step 520 a performance measure is determined for each of the candidate biomarkers, including the filtered reference biomarkers and any derived markers. The performance measure may be of any suitable form and typically includes a correlation or performance explained measure that is indicative of a correlation of the corresponding biomarker and its ability to distinguish between groups. In one example, the performance function used to determine the performance measure is a standard univariate statistical test over all candidate biomarkers. Other examples however include a t-test, a non-parametric equivalent or area under receiver operator curve, chi squared or regression analyses or their equivalents, extensions or derivatives may be used.

The outcome of the applying the performance function to each in the candidate selection step is a ranked list of biomarkers, with the top N ranked biomarkers proceeding to the next stage. The biomarkers falling below this threshold are no longer considered. The threshold applied may be an absolute number or proportion of all biomarkers, or determined by performance, such as a p value <0.05. The threshold should be chosen to contain a sufficiently large number of biomarkers to bias towards including sufficiently independent biomarkers (i.e., low mutual correlation).

At step 525, the processing system 201 selects a next two candidate biomarkers based on the performance measure and on a mutual correlation. In this regard, two markers that are highly correlated with each other in terms of the context of the condition will not necessarily improve the ability to distinguish a particular presence, absence, degree or prognosis of the condition any more than a single one of the markers. Accordingly, it is typical to select biomarkers that have a high performance measure in respect of the condition, but which have a mutual correlation that falls below a mutual correlation threshold. The mutual correlation threshold used will vary depending upon the preferred implementation, and is typically selected to be as low as possible, as described above. Examples of the manner in which the biomarkers are selected will be described in more detail below with respect to FIGS. 6A and 6B.

At step 530, the processing system 201 determines a performance of a next candidate biomarker combination. In this regard, the processing system 201 will use a combining function such as addition, to combine the biomarker values of the selected candidate biomarkers and use this to determine and indicator value based on the combination of biomarker values. The performance can be determined in any suitable manner, such as using statistical measurements, correlation measurements, concordance measurements or aggregate performance measurements such as averages. In one particular example, the performance measure is a ‘variance explained’ (VE). A VE of “1” means that using the biomarkers, you can perfectly classify/predict the disease. A VE of “0.8” means that your markers account for 80% of the result in practice.

Accordingly, at step 535 the processing system 201 compares the performance of the indicator to a performance threshold and determines if this is exceeded at step 540. In the event that the threshold is exceeded, this indicates that the selected combination of markers provides the required degree of discrimination allowing the presence, absence, degree or prognosis of the condition to be determined.

In the event that the threshold is not exceeded at step 540, it is determined if all combinations have been considered at step 545. In this regard, it is possible that multiple different combinations of the two selected biomarkers to be tried, so if each possible combination has not been considered, the processing system 201 returns to step 530 to determine the performance of a next candidate biomarker combination. In this regard, the combinations used will typically be ordered in terms of preference, so that preferred combinations are tried first, with less preferred combinations being tried only in the event that preferred combinations prove unsuccessful.

Once all candidate biomarker combinations have been considered for the two candidate biomarkers selected, and if the performance threshold has still not been exceeded, the process moves onto step 550 to compare the current number of candidate biomarkers being considered to a limit. In this regard, the limit is used to control the overall number of biomarkers in the biomarker signature, thereby minimizing signature size and hence the cost of performing the associated measurements and diagnosis.

If the limit has not been exceeded an additional biomarker is added based on the correlation and performance at step 560, with the process moving onto step 530 to determine a performance of the next candidate biomarker combination. Otherwise if a limit has been reached, then an alternative next two candidate biomarkers are selected at step 525. Thus, this process allows additional candidate biomarkers to be progressively included, with a combination of the multiple candidate biomarkers being compared to the performance threshold, to determine if the required performance is met. If this is not achieved before the number of candidate biomarkers reaches the limit, the process is recommenced using two different candidate biomarkers.

Once the performance threshold has been exceeded at step 540, the selected candidate biomarkers can be defined as signature biomarkers for inclusion in a biomarker signature for the one or more conditions at step 565.

It should be noted that before the biomarker signature is finalized at step 565, additional checks might be performed, to ensure that the candidate biomarkers included in the signature should not be excluded for any reason. For example, candidate biomarkers might be excluded for cost considerations as some combinations of candidate biomarkers may cost more than others. For example, a larger number of biomarkers may cost more than a smaller number, and the additional cost may not be justified by a small improvement in performance. Alternatively, the cost might be increased if multiple different tests are required in order to measure required biomarker values.

Biomarkers might also be excluded from use for legal reasons, for example if their use is restricted for approval or intellectual property reasons. Some biomarkers may be difficult to measure from a technical perspective, for example very low expression in vivo, which increases variability and therefore reduces robustness.

The performance of each biomarker combination panel may also include some variability, typically expressed as confidence intervals around the reported performance. Although a point estimate for one panel may be higher than for another, if the difference given the variability is not significant, the combinations may be considered equivalent.

Once a particular combination of signature biomarkers has been defined, at step 570 the processing system 201 can determine an indicator value range associated with each group. In particular, the range of reference biomarker values for the signature biomarkers within each group are used to calculate indicator value ranges for each group. These can then be compared to an indicator value calculated for a biological subject having an unknown presence absence, degree or progression of the at least one condition and used to identify a group to which the subject would belong and hence the presence, absence, degree or progression of the condition.

Thus, the above-described process iteratively assesses the biomarkers, initially selecting two biomarkers, with various combinations of these being considered to determine if these have the required performance for use in diagnosing the presence, absence, degree or progression of a condition. In the event that the required performance is not provided, additional biomarkers can be added and further combinations tried. Thus the process can consider three biomarkers, four biomarkers, five biomarkers, six biomarkers, seven biomarkers, eight biomarkers, nine biomarkers or more. Typically this is performed to a limit which may be defined based for example on the number of biomarkers that can practically be measured within given cost or process parameters. In the event that the required performance is not obtained the process moves onto to select alternative candidate biomarkers with this being repeated.

Thus it will be appreciated that the above process initially selects those biomarkers which have the suitable performance and which are not highly correlated on the basis that these provide the maximum performance. The ability of these biomarkers to distinguish is then tested and in the event that this is insufficient, further biomarkers can be added to a limit. If this still does not provide the required discriminatory performance alternative biomarkers can be selected.

The process of selecting two candidate biomarkers at step 525 can be achieved in any number of ways depending upon the preferred implementation and examples of this will now be described with reference to FIGS. 6A and 6B.

In the example of FIG. 6A, at step 600 biomarkers are grouped according to their mutual similarity. Thus, highly correlated biomarkers are put together in common groups. Biomarkers within the group are ranked at step 610 based on their performance measure in terms of their correlation with the condition, with the highest ranked biomarkers from two of the groups being selected at step 620 to define the next two candidate biomarkers. It will be appreciated if additional candidate biomarkers are required, these can be selected from different groups to the first two candidate biomarkers.

An alternative process is shown in FIG. 6B. In this example, at step 650 biomarkers are ranked based on their performance at discriminating the condition(s). At step 660 a next highest biomarker is selected, with the remaining biomarkers being re-ranked on the combination of their similarity with the selected biomarker, for example using mutual information, as well as their performance. The next highest biomarker is then selected at step 680 with this process being repeated as required.

Accordingly, it will be appreciated that the above-described processes provide mechanisms for selecting a combination of biomarkers, and more typically derived biomarkers, that can be used to form a biomarker signature, which in turn can be used in diagnosing the presence, absence or degree of at least one condition or in providing a prognosis of at least one condition. In this regard, the biomarker signature defines the biomarkers that should be measured (i.e., the signature biomarkers), how derived biomarker values should be determined for measured biomarker values, and then how biomarker values should be subsequently combined to generate an indicator value. The biomarker signature can also specify defined indicator value ranges that indicate a particular presence, absence, degree or prognosis of one or more conditions.

An example of the method of using a biomarker signature described above will now be described with reference to FIG. 7.

In this example, at step 700 a plurality of measured biomarker values are measured for a biological subject whose condition is unknown, with these typically being provided to the processing system 201, for example by download from measuring equipment or the like.

After any required processing, such as normalization or the like, at step 710, the processing system 201 applies one or more functions to the measured biomarker values to determine any required derived biomarker values. A derived biomarker value and another biomarker value (i.e., another derived biomarker value or a measured biomarker value) are combined to generate an indicator value, which can then be displayed or otherwise used in determining the presence, absence, degree or prognosis of one or more conditions. Thus, this can involve simply displaying the indicator value, allowing an assessment to be made by a medical practitioner or alternatively may involve further processing, such as comparing the indicator to defined indicator value ranges that indicate a particular presence, absence, degree or prognosis of one or more conditions, with the results of the comparison being displayed.

Accordingly, in the above-described method the biomarker signature defines the biomarker values that need to be measured and/or derived, allowing the processing system 201 to automatically generate an indicator value based on received measured biomarker values. Once this has been completed, the processing system 201 can compare the indicator value to the indicator value ranges, and either display results of the comparison, or alternative interpret the results of the comparison, allowing an indicator to be displayed that is indicative of the presence, absence, degree or prognosis of a condition. This can then be used by a medical practitioner as required in performing a medical diagnosis of the biological subject.

Using the above-described methods it has been identified that the use of ratios of “immune system biomarkers” is particularly beneficial when assessing a likelihood of a biological subject having a presence, absence, degree or prognosis of at least one medical condition.

As used herein, the term “immune system biomarker” refers to a biomarker of the host's immune system that is altered, or whose level of expression is altered, as part of an inflammatory response to damage or pathogenic insult, including metabolic, toxic, neurotoxic, iatrogenic, thermal or chemical insults, illustrative examples of which include trauma, surgery, drugs including chemotherapeutic drugs, radiation, disease including pathogenic infection, metabolic disease and ischemia, as well as foreign or implanted substances.

The term “immune system”, as used herein, refers to cells, molecular components and mechanisms, including antigen-specific and non-specific categories of the adaptive and innate immune systems, respectively, that provide a defense against damage and insults and matter, the latter comprised of antigenic molecules, including but not limited to tumors, pathogens, and self-reactive cells.

The term “innate immune system” refers to a host's non-specific reaction to insult to include antigen-nonspecific defense cells, molecular components and mechanisms that come into action immediately or within several hours after exposure to almost any insult or antigen. Elements of the innate immunity include for example phagocytic cells (monocytes, macrophages, dendritic cells, polymorphonuclear leukocytes such as neutrophils, reticuloendothelial cells such as Küpffer cells, and microglia), cells that release inflammatory mediators (basophils, mast cells and eosinophils), natural killer cells (NK cells) and physical barriers and molecules such as keratin, mucous, secretions, complement proteins, immunoglobulin M (IgM), acute phase proteins, fibrinogen and molecules of the clotting cascade, and cytokines. Effector compounds of the innate immune system include chemicals such as lysozymes, IgM, mucous and chemoattractants (e.g., cytokines or histamine), complement and clotting proteins.

The term “adaptive immune system” refers to antigen-specific cells, molecular components and mechanisms that emerge over several days, and react with and remove a specific antigen. The adaptive immune system develops throughout a host's lifetime. The adaptive immune system is based on leukocytes, and is divided into two major sections: the humoral immune system, which acts mainly via immunoglobulins produced by B cells, and the cell-mediated immune system, which functions mainly via T cells.

Accordingly, in one example, an indicator is determined that correlates to a ratio of immune system biomarkers, which can be used in assessing a likelihood of a biological subject having a presence, absence, degree or prognosis of at least one medical condition.

In this example, the method includes determining a pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject.

The biomarker values are used to determine a derived biomarker value using the pair of biomarker values, the derived biomarker value being indicative of a ratio of concentrations of the pair of immune system biomarkers.

Thus, if the biomarker values are the concentrations of the biomarkers, then the derived biomarker value will be based on a ratio of the biomarker values. However, if the biomarker values are related to the concentrations of the biomarkers, for example if they are logarithmically related by virtue of the biomarker values being based on PCR cycle times, or the like, then the biomarker values may be combined in some other manner, such as by subtracting the cycle times to determine a derived biomarker value indicative of a ratio of the concentrations.

The derived biomarker is then used to determine the indicator, either by using the derived biomarker value as an indicator value, or by performing additional processing, such as comparing the derived biomarker value to a reference or the like, as will be described in more detail below.

In any event, combining biomarker values to determine a ratio of concentrations of immune system biomarkers, and then using this to determine an indicator allows indicators to be determined for use in determining a likelihood of a subject suffering from a range of different conditions, depending on the immune system biomarkers selected, which as it will be appreciated can be performed using the above described process.

A number of further features will now be described.

In one example, the process involves determining a first derived biomarker value using a first pair of biomarker values, the first derived biomarker value being indicative of a ratio of concentrations of first and second immune system biomarkers, determining a second derived biomarker value using a second pair of biomarker values, the second derived biomarker value being indicative of a ratio of concentrations of third and fourth immune system biomarkers and determining the indicator by combining the first and second derived biomarker values. Thus, in this example, two pairs of derived biomarker values can be used, which can assist in increasing the ability of the indicator to reliably determine the likelihood of a subject having a condition.

The derived biomarker values could be combined using a combining function such as an additive model; a linear model; a support vector machine; a neural network model; a random forest model; a regression model; a genetic algorithm; an annealing algorithm; a weighted sum; a nearest neighbor model; and a probabilistic model.

In one example, the indicator is compared to an indicator reference, with a likelihood being determined in accordance with results of the comparison. The indicator reference is typically derived from indicators determined for a number of individuals in a reference population. The reference population typically includes individuals having different characteristics, such as a plurality of individuals of different sexes; and/or ethnicities, with different groups being defined based on different characteristics, with the subject's indicator being compared to indicator references derived from individuals with similar characteristics. The reference population can also include a plurality of healthy individuals, a plurality of individuals suffering from at least one diagnosed medical condition, a plurality of individuals showing clinical signs of at least one medical condition and/or first and second groups of individuals, each group of individuals suffering from a respective diagnosed medical condition.

It will be appreciated that the individuals selected will depend on the intended use of the indicator. In particular, when the indicator is for use in determining the likelihood that a biological subject has a specific medical condition, the sample population includes individuals presenting with clinical signs of the specific medical condition, individuals diagnosed with the specific medical condition and healthy individuals. This ensures that the assessment of indicator validity applies regardless of not or whether the individual has the specific condition or not.

It will also be appreciated that the sample population could also include a plurality of individuals of different sexes, ethnicities, ages, or the like, allowing the control value ranges to be common across populations. However, this is not essential, and alternatively control value thresholds could be established that are specific to a particular sub-set of the population. In this case, it would be necessary to ensure that the control value threshold ranges used are appropriate for the subject under consideration.

The indicator can also be used for determining a likelihood of the subject having a first or second condition, in other words to distinguish between the conditions. In this case, this would typically be achieved by comparing the indicator to first and second indicator references, the first and second indicator references being indicative of first and second conditions and determining the likelihood in accordance with the results of the comparison. In particular, this can include determining first and second indicator probabilities using the results of the comparisons and combining the first and second indicator probabilities, for example using a Bayes method, to determine a condition probability corresponding to the likelihood of the subject having one of the conditions. In this situation the first and second conditions could include two medical conditions, or a single medical condition and a healthy condition.

In this case, the first and second indicator references are distributions of indicators determined for first and second groups of a reference population, the first and second group consisting of individuals diagnosed with the first or second condition respectively. In this regard, this can be achieved by determining first and second groups of individuals, each group of individuals having a presence or absence of a diagnosed medical condition and determining first and second indicator references for the first and second groups respectively. This allows the indicator to be used to distinguish between first and second conditions, which could include different medical conditions, as well as healthy conditions. It will also be appreciated that whilst two groups are described, this is not essential and three or more groups could also be defined.

The process is usually performed using at least one electronic processing device, such as a suitably programmed computer system or the like.

In this case, the electronic processing device typically obtains at least two pairs of measured biomarker values, either by receiving these from a measuring or other quantifying device, or by retrieving these from a database or the like. The processing device then determines a first derived biomarker value indicative of a ratio of concentrations of first and second immune system biomarkers and a second derived biomarker value indicative of a ratio of third and fourth immune system biomarkers. The processing device then determines the indicator by combining the first and second derived biomarker values.

The processing device can then generate a representation of the indicator, for example by generating an alphanumeric indication of the indicator, a graphical indication of a comparison of the indicator to one or more indicator references or an alphanumeric indication of a likelihood of the subject having at least one medical condition.

The method would also typically include obtaining a sample taken from the biological subject, the sample including polynucleotide expression products and quantifying at least some of the polynucleotide expression products within the sample to determine the pair of biomarker values. This can be achieved using any suitable technique, and will depend on the nature of the immune system biomarkers.

For example, if the indicator is based on a ratio of concentrations of the polynucleotide expression products, this process would typically include quantifying polynucleotide expression products by amplifying at least some polynucleotide expression products in the sample, determining an amplification amount representing a degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products and determining the indicator by determining a difference between the amplification amounts. In this regard, the amplification amount is generally a cycle time, a number of cycles, a cycle threshold and an amplification time.

In this case, the method includes determining a first derived biomarker value by determining a difference between the amplification amounts of a first pair of polynucleotide expression products, determining a second derived biomarker value by determining a difference between the amplification amounts of a second pair of polynucleotide expression products and determining the indicator by adding the first and second derived biomarker values.

As previously discussed, the at least two immune system biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9 and the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence, degree or prognosis of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3.

Typically the mutual correlation range is one of ±0.8; ±0.7; ±0.6; ±0.5; ±0.4; ±0.3; ±0.2; and, ±0.1.

Typically each immune system biomarker has a condition correlation with the presence, absence, degree or prognosis of the at least one condition that lies outside a condition correlation range, the condition correlation range being between ±0.3 and more typically ±0.9; ±0.8; ±0.7; ±0.6; ±0.5; and, ±0.4. Typically the performance threshold is indicative of an explained variance of at least one of 0.4; 0.5; 0.6; 0.7; 0.8; and, 0.9.

The above-described method has been used to identify 1650 biomarkers of inflammatory response syndromes (also referred to interchangeably herein as “IRS biomarkers” or “IRS immune system biomarkers”), which are useful for assisting in distinguishing: (1) between SIRS affected subjects (i.e., subjects having inSIRS or ipSIRS) and healthy subjects or subjects not affected by SIRS; (2) between subjects with inSIRS and subjects with ipSIRS; and/or (3) between subjects with different stages of ipSIRS (e.g., sepsis, severe sepsis and septic shock). Based on this identification, the present inventors have developed various methods, compositions, apparatus and kits, which take advantage of these biomarkers to provide an indicator for use in diagnosing the presence, absence or degree of at least one condition, or for prognosing at least one condition, wherein the at least one condition is selected from a healthy condition (e.g., a normal condition or one in which inSIRS and inSIRS are absent), inSIRS, ipSIRS, or a stage of ipSIRS (e.g., a stage of ipSIRS with a particular severity, illustrative examples of which include mild sepsis, severe sepsis and septic shock). In advantageous embodiments, the methods and kits involve monitoring the expression of IRS biomarker genes in cells of the immune systems, including blood cells (e.g., immune cells such as leukocytes), which may be reflected in changing patterns of RNA levels or protein production that correlate for example with the presence of active disease or response to disease.

The IRS biomarkers are expression products of genes (also referred to interchangeably herein as “IRS biomarker genes” or IRS immune system biomarker genes”), including polynucleotide and polypeptide expression products. As used herein, polynucleotide expression products of IRS biomarker genes are referred to herein as “IRS biomarker polynucleotides.” Polypeptide expression products of the IRS biomarker genes are referred to herein as “IRS biomarker polypeptides.” The term “gene”, as used herein, refers to a stretch of nucleic acid that codes for a polypeptide or for an RNA chain that has a function. While it is the exon region of a gene that is transcribed to form RNA (e.g., mRNA), the term “gene” also includes regulatory regions such as promoters and enhancers that govern expression of the exon region.

Suitably, the IRS biomarker genes are selected from the group consisting of: PRKCZ, SKI, RER1, TAS1R1, VAMP3, AGTRAP, VPS13D, KLHDC7A, NBL1/C1orf151, MDS2, RCAN3, LDLRAP1, MAN1C1, SH3BGRL3, DHDDS, HCRTR1, CCDC28B, LCK, ZNF362, THRAP3, PPIE//CCDC25, CAP1, CTPS, C1orf84, FAAH, DMBX1, CYP4B1, BTF3L4, LRRC42, C1orf175//TTC4, TMEM61, FPGT//TNNI3K, ACADM, SPATA1, EPHX4, RPAP2, RPL5//SNORA66//SNORD21//FAM69A, RTCD1, SLC30A7, RNPC3//AMY2B, CELSR2, AHCYL1, CEPT1//DRAM2, CHIA, LIX1L, UPF0627, MRPS21, TNFAIP8L2, SMCP, DCST1, RAG1AP1, C1orf182, HAPLN2, NTRK1, CD1E, TOMM40L//NR113, POU2F1, TIPRL, SFT2D2, CACNA1E, SMG7, OCLM, RGS2, ZC3H11A//RP11-74E24.2, MFSD4, IL20, RPS6KC1, C1orf95, ARF1, GALNT2, TNFRSF4, NADK, FLJ14100//C1orf86, GPR153, RERE, SLC2A7, SDHB, RNF186, DDOST, GPN2, RPA2, PEF1, PTP4A2, TRIM62, PHC2, LSM10, MRPS15, RRAGC, COL9A2, TESK2, NRD1, KTI12, CC2D1B, YIPF1, JAK1, SLC35D1, DIRAS3, ZZZ3, GNG5, ZNHIT6, ODF2L, SEP15, BARHL2, GCLM, CLCC1/GPSM2//C1orf62, SORT1, SLC16A4, PHTF1, RSBN1, DENND2C//BCAS2, CD58, SPAG17//WDR3, REG4//NBPF7, RP11-94I2.2//NBPF16//NBPF11//NBPF15//NBPF8//NBPF20//NBPF10//NBPF14//NBPF1//LOC100288142//NBPF12//KIAA1245//LOC100290137, APH1A, POGZ, TDRKH, THEM4, S100A11, CRNN, SPRR2C, S100A12, S100A8, GATAD2B//PLIN2, DENND4B, PBXIP1, PYGO2, SHC1, DCST2, GBA//GBAP, ASH1L, RITZ, MEF2D, AIM2, COPA, DEDD, TADA1L, GPA33, CD247, F5, PIGC, KIAA0040, TOR1A1P2//TOR1A1P1//1FRG15, STX6//KIAA1614, EDEM3, UCHL5, DENND1B, DDX59, KIF21B, ARL8A, CYB5R1, MYBPH, CHI3L1, PIK3C2B//LOC100130573, NUAK2, NUCKS1, FAIM3, PLXNA2, SLC30A1, LPGAT1, ANGEL2, RAB3GAP2//AURKAPS1/AURKA//SNORA36B, TP53BP2, NVL, TMEM63A, PARP1, ITPKB, TARBP1, CHML, AKT3, SMYD3, AHCTF1, OR1C1, NCOA1, HADHB, ABHD1//PREB, SPAST, SLC30A6//DDX50, CRIPT, MSH2, FOXN2, CCDC104, VRK2, AHSA2//USP34, OTX1, AFTPH, CEP68, PLEK, ANXA4, MXD1, NAGK, SMYD5//NOTO, MTHFD2, TTC31, SEMA4F, TMSB10, SH2D6, GNLY, KCNIP3, CNNM4, CNNM3, ZAP70, LIP T1//MRPL30, MAP4K4, IL1R2, IL1R1, IL18R1, POLR1B, CHCHD5, IL1RN, PSD4, DDX18, INSIG2, TMEM177//LOC100125918, RALB, PROC, GPR17//LOC100291428//LIMS2, IMP4, FAM123C, ACVR2A, MBD5, LYPD6B, SLC4A10, UBR3, HAT1, ITGA6, ZAK, OSBPL6, PLEKHA3, ZC3H15, COL3A1, GLS, OBFC2A, COQ10B, MARS2, CFLAR, NOP58, FAM117B, CYP20A1, FASTKD2, PIKFYVE, C2orf62, SLC11A1, AGFG1, CHRNG, EIF4E2, TRPM8, LRRFIP1, GAL3ST2, TMEM18, LAPTM4A, SF3B14, TP53I3, UNQ2999, GPR113//SELI, MPV17, PPM1G, NLRC4, CDC42EP3, HNRPLL, COX7A2L, KCNG3, CALM2//C2orf61, BCL11A, XPO1, NAT8B, DUSP11, MOGS, SNRNP200, SEMA4C, MITD1, ILIA, SLC35F5, CCDC93, CLASP1, SAP130, YSK4, GTDC1, ORC4L, NR4A2//FLJ46875, DPP4, GALNT3, SCN7A, FRZB, STK17B, CLKH/PPIL3, MPP4, INO80D, KLF7, FAM119A, NGEF, ARL4C, RAB17, HDLBP, LRRN1, SETD5, IRAK2, C3orf42, TSEN2, NR2C2//MRPS25, UBE2E1, C3orf35, SNRK, ZNF197, GNAI2, ALAS1, PRKCD, CACNA1D, PXK, PTPRG, ATXN7, SLC35A5, SLC15A2, CCDC48, DNAJC13, CLDN18, GYG1, SELT, MED12L, RAP2B, MYNN, ABCF3, VPS8, HRG, EIF4A2//SNORA4, LPP, CCDC50, LOC152217, TADA3L, SEC13, TIMP4, METTLE, DAZL//DAZ4//DAZ3//DAZ2, SATB1//TBC1D5, SCN10A, SEC22C, ZDHHC3, ZDHHC3, SLC6A20, UQCRC1, PRKAR2A, IMPDH2, CCDC71, UBA7, CAMKV, WDR82, LMOD3, FOXP1, MORC1, ATG3, GSK3B//LOC100129275, HCLS1, KPNA1, PTPLB, C3orf22, RPN1, KIAA1257//ACAD9//LOC100132731, FOXL2, MECOM, PLD1, GNB4, MRPL47, KLHL6, THPO, ETV5, BCL6//LOC100131635, ATP13A5, TMEM44, KIAA1530, TACC3, CNO, BST1, KLF3, TMEM33//DCAF4L1, KIT, ENAM, FAM47E//STBD1, ENOPH1, PDLIM5, CCDC109B//HIGD1A//CCDC13, EGF, PCDH10, RAB33B, TMEM184C, RBM46, GRIA2, C4orf39, KLHL2, TLL1, F11, SLBP, HAUS3//POLN, PPARGC1A, TLR10, C4orf34, TXK, RPL21P44, KDR, RCHY1, CNOT6L, PLACE, HPSE, GPRIN3, PPA2, COL25A1, C4orf3, QRFPR, MFSD8, MAP9, PDGFC, TKTL2, ACSL1, SUBH/TMEM183A, CARD6, MCCC2, TNPO1, PDE8B, PAPD4, THBS4, FAM151B, RASGRF2, SNX2, LMNB1//PCIF1, MEGF10, LEAP2, TCF7, KDM3B, CXXC5, SLC4A9, ANKHD1-EIF4EBP3//ANKHD1//EIF4EBP3, KIAA0141, GRPEL2, MFAP3, GABRA6, GABRA1, DOCK2, RANBP17//USP12, ERGIC1, ATP6V0E1//SNORA74B, ZNF346, NSD1, CLPTM1L, UGT3A1, GDNF, TTC33, hCG_2039148, MOCS2, SLC38A9, CCDC125, ANKRA2, HAPLN1, CCNH, TMEM161B, MBLAC2, MCTP1, TICAM2//TMED7//TMED7-TICAM2, KIF3A, C5orf15, SKP1, CXCL14, KLHL3, CD14, YIPF5, LARS, DCTN4, CCDC69, ATOX1, TIMD4, ADAM19, SLIT3, RNF44, DOK3, MGAT4B//SQSTM1, C5orf45//SQSTM1, RASGEF1C, MGAT1, IRF4, HIVEP1, E2F3, HIST1H4I, HIST1H2BM, MOG, ZNRD1//NCRNA00171, TRIM15, HCG27, BAT2//SNORA38, CYP21A2, ITPR3, MAPK14, MAPK13, PNPLA1, SFRS3, CDKN1A, FOXP4, CUL9, RUNX2, ZNF451, SOBP, C6orf182, KIAA1919, RWDD1, KPNA5, TPD52L1, ARG1, RAB32, ARID1B, SLC22A3, SERPINB1, C6orf146, GCM2, ATXN1, DCDC2//KAAG1, HIST1H3I, HIST1H4L, GABBR1, RNA243, DDAH2, CLIC1, NEU1, RXRB, VPS52, TCP11, CLPS, PGC, ZNF318, YIPF3, MRPL14, PLA2G7, PKHD1, IL17F, HTR1B, GABRR2, UBE2J1, BACH2, MCM9, VNN1, IL20RA, FLJ27255, T, RPS6KA2, HGC6.3, UNC84A//C7orf20, SDK1, ZDHHC4, C7orf26, GLCCI1//tcag7.903, GPNMB, CCDC126, WIPF3//ZNRF2//LOC441208, GPR141, STARD3NL, POU6F2, CDC2L5, ZMIZ2, UPP1, ZNF273, KCTD7//RABGEF1, RABGEF1//tcag7.967//tcag7.951//KCTD7//LOC100293333, CCDC132, PVRIG//PILRB//STAG3, PILRB//PVRIG//STAG3, C7orf51, GNB2, LRRC17, LRRN3, CFTR, LSM8, LUC7L2, MGAM//LOC100124692, GIMAP7, INSIG1, RBM33, ICA1, FAM126A, HIBADH, TRIL, SCRN1, ELMO1, INHBA, CAMK2B, NPC1L1, DDC//LOC100129427, NSUN5//NSUN5B//NSUN5C, CLDN3, C7orf23//DMTF1, SRI, BET1, MCM7, GATS, ATXN7L1//RINT1//EFCAB10, KIAA1549, SLC37A3, SMARCD3, MLL3//BAGE2, CLN8, MSRA, PIWIL2, NEFM//LOC100129717, EPHX2, LEPROTL1, MAK16//C8orf41, AP3M2, FNTA, SGK196, UBE2V2, FLJ46365, SNTG1, TRIM55, C8orf45, PREX2, PLEKHF2, BAALC//FLJ10489, TTC35, MTBP, ZHX2, RNF139, TG, DENND3//C8orf60, TNFRSF10D, TRIM35, GSR, WHSC1L1, PCMTD1//PXDNL, NCOA2, TRAM1//LOC286190, RUNX1T1, EXT1, DDEF1IT1, CDC37L1, UBE2R2, UBAP1//KIF24, GALT, RGP1//GBA2, TGFBR1, C9orf6//IKBKAP, IMAGE5303689, ATP6V1G1, TLR4, SET, MRPL41, C9orf68, HAUS6//SCARNA8, KLHL9, C9orf82, NDUFB6//DFFB, SIT1, FAM108B1, TRPM6, FRMD3, SLC28A3, BICD2, C9orf84, AKNA, MEGF9, C5, GOLGA1//SCAI, SH2D3C, FAM102A, FLJ10232, ASB6, BAT2L, EDF1, FBXW5, C10orf18, FBXO18, GATA3, CUGBP2, VIM, STAM, WAC, BAMBI, ZNF487//LOC439911, ALOX5, WDFY4, SRGN, CCDC109A, FAM149B1//FAM149B2, MINPP1, PTEN//PTENP1, ENTPD1//C10orf131, ABCC2, SFXN2, SHOC2, ACSL5, BCCIP//DHX32, FAM188A, CUBN, SVIL//hCG_1783494, FAM13C//PHYHIPL, ATAD1, ANKRD22, FLJ34077, COX15, ERLIN1, ACTR1A, ABLIM1, RAB11FIP2, C10orf84, PRDX3, C10orf119, NSMCE4A, TALDO1//INTS8, TNNT3, FXC1, PDE3B, DNAJC24, PTPRJ//OR4B1, C11orf31, TMEM109, CD6, CD5, TMEM138, POLR2G, TMEM179B, NAT11, OTUB1, RBM14//RBM4, AIP, PPFIA1, IL18BP//NUMA1, C11orf30, C11orf82, TMEM126B, C11orf73, PIWIL4, LOC100132686, PAFAH1B2, UBE4A, TRAPPC4, SC5DL, VWA5A//OR10D1P, STT3A, VPS26B, TRIM21, ZBED5, SAAL1, FANCF, LIN7C, PHF21A, CUGBP1, OSBP, CYBASC3, TUT1, SLC25A45, LTBP3, EIF1AD, GAB2, CREBZF, PICALM, SLC36A4, CCDC82, KIAA1826, MPZL3, MPZL2, H2AFX, SIAE, ZBTB44, HSN2, ADIPOR2, NCAPD2//SCARNA10//FADS1, PTPN6, CLEC4D, CDKN1B, GOLT1B, FAR2, FGD4, TMEM106C, TMBIM6, C12orf62, PRR13//PCBP2, DGKA, COQ10A, TSPAN31, CDK4/MARCH9/C3HC4, LEMD3, IRAK3, TMTC3, ACTR6, TCTN1, PXMP2//PGAM5, DCP1B, SLC2A3//SLC2A14, C3AR1, PLBD1, TM7SF3, ASB8//PHB, LMBR1L, FMNL3//PRPF40B, AAAS, NFE2, GPR84, CD63, SARNP//DNAJC14, NACA, CDK4//TSPAN31, TMBIM4//LOC100133322, IL22, LIN7A, HAL, APPL2, GLTP, GIT2, VPS29, PPTC7, DDX54//CCDC42B, SLC24A6, SDS, RBM19, MED13L, C12orf49, FBXO21, WSB2, TAOK3, CIT, RAB35, RPLP0, PXN, TRIAP1, SFRS9, POP5, UNQ1887, C12orf43, ANAPC5, KDM2B, MORN3, TMEM120B//RHOF, LOC338799, DIABLO//B3GNT4, VPS33A, CLIP1, PITPNM2, EIF2B1, CCDC92, NCOR2, DHX37, DDX51, POLE, GOLGA3, ZMYM2, SPATA13//C1QTNF9, NUPL1, PAN3//EEF1A1//CHCHD2, ALOX5AP, EEF1DP3, KL, UFM1, NARG1L, ITM2B, FNDC3A, CDADC1, ARL11, LMO7, DNAJC3, TM9SF2, CLYBL, PCCA, ABHD13, LAMP1, TMCO3, UPF3A, ZMYM5//ZMYM2, ZDHHC20//LOC728099, PARP4, MTMR6//LOC646482, HSPH1, N4BP2L2//CG030, ELF1, LCP1, KPNA3, C13orf1, DLEU2//DLEU2L, GUCY1B2, INTS6, DACH1, TBC1D4, EDNRB, UGGT2, GPR183, LIG4, ANKRD10, RASA3, RNASE2//LOC643332, RPGRIP1, IRF9, TSSK4, C14orf21, SCFD1, FANCM, ABHD12B, PTGDR, FBXO34//KIAA0831, C14orf101, ACTR10, ARID4A, JKAMP, HIF1A, SYNE2, EXD2, SLC39A9, SFRS5, PCNX, SIPA1L1//SNORD56B//LOC145474//LOC283567, YLPM1, BATF, FLVCR2//RPS24, GPR65, TDP1, EVL, ZNF839, TDRD9, INF2, PLD4, MTA1//LOC647310//LOC100128343, NDRG2, DAD1//OR6J1, SLC7A8, IPO4, TM9SF1, ADCY4, RIPK3, EAPP, BAZ1A, NFKBIA, SEC23A, C14orf104, C14orf138, SOS2, NIN, PYGL, CNIH, DHRS7, WDR89, ACTN1, NUMB, C14orf43, ABCD4, KIAA0317, NEK9, ANGEL1, SPTLC2, SERPINA6, DICER1, BCL11B, ANKRD9, PPP1R13B, AKT1, BRF1, TUBGCP5, SNRPN//SNURF//IPW//SNORD116-16//SNORD116-18//SNORD116-21//SNORD116-22//SNORD116-17//SNORD116-19//PAR5//PAR-SN//SNORD116-2//SNORD116-25//SNORD116-26//SNORD107//SNORD115-12//SNORD115-5//SNORD115-6//SNORD115-9//SNORD116-11//SNORD116-12//SNORD116-13//SNORD116-28//SNORD116-4//SNORD64//PAR1//SNORD109A//SNORD109B//SNORD116-6//SNORD116-3//SNORD116-9//SNORD115-13//SNORD115-1//SNORD115-14//SNORD115-15//SNORD115-21//SNORD115-10//SNORD115-7//SNORD115-16//SNORD115-40//SNORD115-42//SNORD115-11//SNORD115-29//SNORD115-34//SNORD115-36//SNORD115-4//SNORD115-43//HBII-52-24//SNORD116-5//SNORD116-7//SNORD115-26//SNORD115-30//SNORD116-15//SNORD116-8//SNORD115-2//SNORD115-39//SNORD116-14//SNORD116-20//SNORD115-8//SNORD115-3//SNORD115-38//SNORD115-41//SNORD115-22//SNORD115-44//SNORD116-1//SNORD115-17//SNORD115-18//SNORD115-19//SNORD115-20//SNORD116@, APBA2, MTMR15//MTMR10, RYR3, BAHD1, CHP, JMJD7-PLA2G4B//JMJD7//PLA2G4B, HAUS2, C15orf63//SERF2, B2M, TRIM69, PLDN, SQRDL, GALK2, USP8, GLDN, MAPK6, LACTB, RAB8B, APH1B, USP3//LOC100130855, SNX1, LBXCOR1/PIAS1//CALML4, NEO1, MPI, FBXO22//FBXO22OS, RCN2, FAH, IL16, ABHD2, SLCO3A1, MCTP2, MEF2A//LYSMD4, NIPA2//CYFIP1, HERC2//HERC2P2//HERC2P3//LOC440248, MTMR10//MTMR15, C15orf24, SLC12A6, LPCAT4, INO80, OIP5, ZFP106, CDAN1, SPG11//ISLR, SPPL2A, GNB5//LOC100129973, MYO5A, ARPP19, RAB27A, CCPG1//PIGB//DYX1C1, BNIP2, CA12, FAM96A, KIAA0101//CSNK1G1, TLE3, PARP6, NPTN, MAN2C1, IMP3, MTHFS, ST20//C15orf37, TMC3, AP3B2, C15orf40, WDR73, NTRK3, DET1, TM2D3, WDR90, RHOT2//FBXL16, TMEM204, CRAMP1L//HN1L, MAPK8IP3, TBL3, TSC2, KCTD5//PRO0461//PDPK1, CLUAP1, DNASE1, DNAJA3, CP110, C16orf62, LYRM1, METTL9, EEF2K, POLR3E, PLK1, PRKCB, IL21R//LOC283888, SULT1A2//SULT1A1, ATXN2L, LAT//SPNS1//NPIPL2//LOC728741//LOC730153//NPIPL3//SPIN1/LOC728888//LOC100289169//LOC728734//LOC729602//LOC100288442//LOC100288332, KIF22, MAZ, CORO1A//LOC606724, ITGAL, SRCAP//SNORA30, ZNF646//ZNF668, C16orf67, TMEM188, LPCAT2, CETP, CKLF, CMTM1//CKLF, TMEM208, CTCF, THAP11, NUTF2, EDC4, SLC7A6//SLC7A6OS, PRMT7, SNTB2, VPS4A, DDX19B//DDX19A, CHST4, HP//HPR, PLCG2, KLHL36, KIAA0182, BANP//RUNDC2C, TRAPPC2L, SPG7, CDK10, TCF25, AFG3L1, LUC7L, AXIN1, JMJD8, LMF1, UNKL, UNKL, CLCN7, MRPS34, RNPS1, NLRC3, TRAP1//DNASE1, ADCY9, CORO7, C16orf72, RRN3//LOC653390//LOC730092//LOC100131998, XYLT1/LYRM2//ZC3H11A, DCUN1D3//LYRM1, IGSF6//METTL9, CDR2//RRN3//LOC100131998//LOC653390, COG7, GGA2, NSMCE1, GTF3C1, CCDC101//LOC388242, C16orf54, KCTD13, SEPT1, ZNF764//ZNF747, C16orf58//LOC100128371, ITFG1, ABCC11//LONP2, NUDT21, BBS2//OGFOD1, CSNK2A2, GOT2, FAM96B, FHOD1//SLC9A5, ATP6VOD1//LOC100132855, GFOD2, SLC12A4, DPEP3, DPEP2, CHTF8//HAS3, COG8//PDF, TERF2, AARS, ST3 GAL2, VAC14//LOC100130894, APIG1, WDR59, CTRB2//CTRB1, TAF1C//ADAD2, FBXO31, ZCCHC14, FAM38A, CENPBD1, TIMM22, RPA1, DPH1//OVCA2, SGSM2, ARRB2, LOC100130950, DNAH2, PIGL, TRPV2, MPRIP, DRG2, ALKBH5//FLJ13773, SMCR7, WSB1, TAOK1, CPD, SUZ12P, RNF135, ZNF830, TAF15, GGNBP2, LASP1, PSMD3, CDC6, NBR2, TMUB2, MGC57346//C17orf69, NSF//LOC728806, GOSR2, NPEPPS//TBC1D3F//LOC440434, KPNB1, CDK5RAP3, ATP5G1, UBE2Z, XYLT2//LOC100130580, NOG, DGKE, AKAP1, TMEM49//CLTC//MIR21, CLTC, CA4, C17orf64, DCAF7, PITPNC1, NOL11//SNORA38B, MAP2K6, COG1, CD300A, TMEM104, MRPS7, KIAA0195, T5EN54, LLGL2, LOC100134934//CDK3, MFSD11, SEPT9, TNRC6C, TMC8, ENGASE, RPTOR, GPS1, FN3KRP, TBCD, GEMIN4, GLOD4, SLC43A2, PRPF8, SMG6//C17orf6, METT10D//LOC284009, SHPK, TAX1BP3, P2RX5, MYBBP1A//SPNS2, PELP1, PFN1, ZNF232, DHX33, DERL2, NLRP1//LOC728392, ASGR2, NEURL4//GPS2//D4S234E, ZBTB4, TP53, VAMP2, PIK3R5, ELAC2, NCOR1//C20orf191//LOC100131704, ZNF287, TOM1L2//LOC246315, GRAP//SNORD3B-1//SNORD3B-2//LOC400581, ALDOC, SDF2, RAB34, PHF12, NUFIP2, OMG, EVI2B, C17orf66//RSL24D1, SYNRG//LOC100131822, PLXDC1, CACNB1, PGAP3, MED24, NR1D1//THRA, CCR7, STAT5B//STAT5A, FAM134C, VAT1, DUSP3, C17orf65//ASB16, UBTF, GPATCH8, MAP3K14//LOC100133991, OSBPL7, SLC35B1, TOB1, COX11//TOM1L1, VEZF1, SFRS1//FLJ44342, SEPT4, MED13//LOC100129112, LIMD2//MAP3K3, STRADA, FTSJ3, CD79B, ICAM2, ERN1, TEX2, LRRC37A3//LRRC37A2//LRRC37A//ARL17P1//LRRC37A4//LOC100294335//LOC644397, GNA13, WIPI1//ARSG, FAM20A, NAT9, GGA3, H3F3B//H3F3C, EXOC7, SFRS2, TMC6//LOC100131096, USP36, CD7, RAB31, VAPA, SEH1L, HQ0644/PRO0644, RNMT, RNF138, GALNT1, ELP2, PIK3C3, SLC14A2, ME2, SERPINB2//SERPINB10, ZNF407, ZNF236, NFATC1//LOC100127994, ENOSF1//TYMS, MYOM1, AFG3L2, ABHD3, OSBPL1A, CDH2, DSC1, PSTPIP2, C18orf32, MBD2//SNORA37, PIGN, TMX3, PQLC1, GZMM, ARID3A, CIRBP, DAZAP1, SPPL2B, NFIC, VAV1, ARHGEF18//LOC100128573, STXBP2//L00554363//LOC100131801, C19orf59, ZNF317, ILF3, SMARCA4, PRKCSH, IER2, CCDC130, DCAF15, IL27RA, KLF2, SIN3B, DDA1, GTPBP3, FAM129C, FCHO1, ARRDC2, IFI30, C19orf60, CRTC1//MAML2, RFXANK//MEF2B//LOC729991, ZNF101, ZNF738, ZNF257//ZNF492//ZNF99//ZNF98//LOC646864, C19orf2, KIAA0355//FLJ21369, USF2, TMEM147, LIN37//PSENEN, C19orf55, TBCB//POLR2I, ZNF382, ZNF568, ZNF420, ZNF383, CCDC97, ZNF574, CD177, ZNF230//ZNF222, VASP, GRWD1, FLT3LG, ZNF175, NCRNA00085, PPP2R1A, ZNF808//ZNF578//ZNF611, LENG8, FCAR, RPL28, U2AF2, LOC100288114//MGC9913, ZFP28, ZNF460, ZNF549, ZNF211, ZNF587//ZNF417, ZNF274, ZNF544, ZNF8, TRIM28, C19orf6, C19orf34, GNG7, AES, EEF2//SNORD37, PLIN5ARG1, PLIN3, PTPRS, SAFB2//SAFB, RANBP3, GTF2F1//LOC100130856, XAB2, ELAVL1, ADAMTS10, FBXL12, DNMT1, TYK2, KEAP1, KRI1, TMEM205//hCG_29977, ZNF563, MAN2B1//MORG1, C19orf56, DHPS, TNPO2//SNORD41, LPHN1, NDUFB7, AKAP8, AKAP8L, CHERP//C19orf44//CALR3, INSL3//JAK3, IL12RB1, UPK1A, TYROBP, ZNF529, ZNF461, ZNF607, YIF1B, PRR13, CEACAM4, PLAUR, TRAPPC6A, ERCC1//CD3EAP, RTN2, SYMPK, PGLYRP1, NOSIP, PNKP, NKG7, FPR1, ZNF28, OSCAR, MBOAT7, LILRA5, LILRA4, ZNF550//ZNF549, ZNF416, ZNF256, ZNF329, FAM110A, ITPA, CDC25B, CDS2, CRLS1, CSRP2BP, SEC23B, SLC24A3, HCK, ASXL1, ACSS2, C20orf4, TGIF2, C20orf24//SLA2, RPN2//EEF1A2, CTNNBL1, ACTR5, PPP1R16B, DHX35, PLCG1, MYBL2, SYS1//SYS1-DBNDD2//DBNDD2, DNTTIP1, CTSA, MMP9//LOC100128028, DDX27, SLC9A8, RNF114, PTPN1, TSHZ2, PFDN4, CSTF1, CASS4, GNAS, C20orf177, CDH26, C20orf197, LOC284757, ARFGAP1, PRPF6, NSFL1C, SIRPD, SIRPG//SIRPA, RNF24, RASSF2, TMX4, JAG1, C20orf74, C20orf3, C20orf112, CDK5RAP1, AHCY, GGT7, EDEM2, RBM39//LOC643167, BLCAP, SERINC3//TTPAL, ZNF335, ELMO2, B4GALT5, DPM1, ZFP64, ZNF217, CTSZ, SYCP2, PSMA7, DIDO1, YTHDF1, CHODL, BACH1, C21orf41//BACH1, IL10RB, IFNAR1, IFNGR2, SON, MORC3//DOPEY2, DYRK1A, KCNJ15, ETS2, RRP1B, PFKL, TRPM2, ADARB1, SAMSN1//LOC388813, N6AMT1, SYNJ1, TMEM50B, KCNE1, PRDM15, C2CD2, WDR4, U2AF1, CSTB, UBE2G2//SUMO3, PTTG11P, POFUT2, MCM3AP, IL17RA//CECR7, C22orf37, LZTR1, PPIL2//YPEL1, CYTSA, SNRPD3//C22orf13, NF2, LIMK2, SLC5A1, MCM5, NCF4, GGA1, SH3BP1//PDXP, POLR2F//LOC100131530, APOBEC3A//APOBEC3B, APOBEC3D, ATF4, CACNA1I, ZC3H7B, CCDC134, TSPO, NUP50, TBC1D22A//LOC100289878, RP3-402G11.5, SAPS2, NCAPH2, BID, SLC25A1, KLHL22//KRT18, PI4KA//PI4KAP1//PI4KAP2//LOC100293141, MAPK1, ZNF70, TPST2, SF3A1//CCDC157, PES1, PIK3IP1, PATZ1, C22orf30, IL2RB, CSNK1E//LOC400927, UNC84B, CBX7//LOC100128400, RPS19BP1, MKL1//KIAA1659, RANGAP1, TCF20, LDOC1L, UNQ6126, TUBGCP6, SBF1//SBF1P1, MSL3, MOSPD2, BMX//HNRPDL, PDHA1, YY2, PDK3, GK//GK3P//FTL//LOC652904, CXorf59, ATP6AP2, USP9X//USP9Y, RP2, USP11, RBM3, FTSJ1, WAS, PLP2, TSPYL2//GPR173, MAGED2, UBQLN2, NLGN3, ACRC, UPRT, CXorf26, ATP7A, DIAPH2, CSTF2//RAD21, ARMCX3, ARMCX5, GPRASP1, TMEM31, TBC1D8B, MID2, DOCK11, LONRF3, UBE2A, SH2D1A, OCRL, SLC25A14, HPRT1, CD40LG, AFF2, SSR4//IDH3G, FAM50A, DKC1//SNORA36A//SNORA56, ARSD, KAL1, CTPS2, RPS6KA3, BCOR, MAOB//NAT13, ZNF41, OTUD5, KCND1, ZMYM3, MAGT1, BRWD3, TRMT2B, GLA, MORF4L2, PSMD10, ACSL4, LAMP2, CUL4B, ODZ1, ELF4, RAP2C, FAM127B//FAM127C//FAM127A, TMEM185A, ARD1A, IRAK1, DNASE1L1//RPL10, SH3KBP1, Mitochondrial, Mitochondrial, CCNL2, INPP5B, TLR5, ADRB3//GOT1L1, NOC2L//SAMD11//LOC401010 and SHFM1 (hereafter referred to interchangeably herein as “the full list of IRS immune system biomarker genes” or “full list IRS biomarker genes”).

The methods, compositions, apparatus and kits of the present invention take advantage of the IRS biomarkers broadly described above and elsewhere herein to provide an indicator for use in diagnosing the presence, absence or degree of the at least one condition selected from a healthy condition (e.g., a normal condition or one in which inSIRS and inSIRS are absent), inSIRS, ipSIRS or a stage of ipSIRS (e.g., a stage of ipSIRS with a particular severity such as mild sepsis, severe sepsis and septic shock), or in providing a prognosis of the at least one condition, which may involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values (also referred to herein as a “biomarker signature”), the indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition, wherein: (i) at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3.

In advantageous embodiments, the diagnostic or prognostic methods, compositions, apparatus and kits of the present invention involve: (1) determining a plurality of measured IRS biomarker values, each measured IRS biomarker value being a measured value of an IRS biomarker of the biological subject; and (2) applying a function to at least one of the measured IRS biomarker values to determine at least one derived IRS biomarker value, the at least one derived IRS biomarker value being indicative of a value of a corresponding derived IRS biomarker. The function suitably includes at least one of: (a) multiplying two IRS biomarker values; (b) dividing two IRS biomarker values; (c) adding two IRS biomarker values; (d) subtracting two IRS biomarker values; (e) a weighted sum of at least two IRS biomarker values; (f) a log sum of at least two IRS biomarker values; and (g) a sigmoidal function of at least two IRS biomarker values.

In some embodiments, the diagnostic or prognostic methods, compositions, apparatus and kits involve: determining at least one derived IRS biomarker value corresponding to a ratio of two measured IRS biomarker values. In these examples, the diagnostic or prognostic methods, apparatus and kits suitably include combining at least two IRS biomarker values to determine an indicator value representing the indicator and in illustrative examples of this type, the at least two IRS biomarker values are combined using a combining function (e.g., any one or more of: an additive model; a linear model; a support vector machine; a neural network model; a random forest model; a regression model; a genetic algorithm; an annealing algorithm; weighted sum; a nearest neighbor model; and a probabilistic model). Suitably, the diagnostic or prognostic methods, apparatus and kits include: (a) determining a first derived IRS biomarker value, the first derived IRS biomarker value being a ratio of first and second measured IRS biomarker values; (b) determining a second derived IRS biomarker value, the second derived IRS biomarker value being a ratio of third and fourth measured IRS biomarker values; and (c) adding the first and second derived IRS biomarker values to generate an indicator value.

In some embodiments, the methods, compositions, kits and apparatus of the present invention are useful for diagnosing that inSIRS or a healthy condition is present or absent in the biological subject, which suitably involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from inSIRS and a healthy condition, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group A IRS biomarker genes as defined herein, and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group B IRS biomarker genes as defined herein.

In other embodiments, the methods, apparatus and kits are useful for diagnosing that ipSIRS or a healthy condition is present or absent in the biological subject, which suitably involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from ipSIRS and a healthy condition, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group C IRS biomarker genes, as defined herein, and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group D IRS biomarker genes as defined herein.

In still other embodiments, the methods, apparatus and kits are useful for diagnosing that inSIRS or ipSIRS is present or absent in the biological subject, which suitably involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from inSIRS and ipSIRS, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group E IRS biomarker genes as defined herein, and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group F IRS biomarker genes as defined herein.

In other embodiments, the methods, apparatus and kits are useful for diagnosing that inSIRS or ipSIRS is present or absent in the biological subject, which suitably involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from inSIRS and ipSIRS, wherein: (i) at least four IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least four IRS biomarkers is selected from a first IRS biomarker group, wherein at least one other of the at least four IRS biomarkers is selected from a second IRS biomarker group, wherein at least one other of the at least four IRS biomarkers is selected from a third IRS biomarker group, and wherein at least one other of the at least four IRS biomarkers is selected from a fourth IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group G IRS biomarker genes as defined herein, wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group H IRS biomarker genes as defined herein, wherein the third IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group I IRS biomarker genes as defined herein, and wherein the fourth IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group J IRS biomarker genes as defined herein.

In still other embodiments, the methods, apparatus and kits are useful for diagnosing that mild sepsis or severe sepsis is present or absent in the biological subject, which suitably involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from mild sepsis and severe sepsis, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group K IRS biomarker genes as defined herein, and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group L IRS biomarker genes as defined herein.

In still other embodiments, the methods, apparatus and kits are useful for diagnosing that mild sepsis or septic shock is present or absent in the biological subject, which suitably involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from mild sepsis and septic shock, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group M IRS biomarker genes as defined herein, and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group N IRS biomarker genes as defined herein.

In still other embodiments, the methods, apparatus and kits are useful for diagnosing that severe sepsis or septic shock is present or absent in the biological subject, which suitably involve: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, degree or prognosis of the at least one condition selected from severe sepsis and septic shock, wherein: (i) at least two IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker group and wherein at least one other of the at least two IRS biomarkers is selected from a second IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group O IRS biomarker genes as defined herein, and wherein the second IRS biomarker group consists of polynucleotide and/or polypeptide expression products from group P IRS biomarker genes as defined herein.

As used herein, the terms “diagnosis”, “diagnosing” and the like are used interchangeable herein to encompass determining the likelihood that a subject will develop a condition, or the existence or nature of a condition in a subject. These terms also encompass determining the severity of disease or episode of disease, as well as in the context of rational therapy, in which the diagnosis guides therapy, including initial selection of therapy, modification of therapy (e.g., adjustment of dose or dosage regimen), and the like. By “likelihood” is meant a measure of whether a biological subject with particular measured or derived biomarker values actually has a condition (or not) based on a given mathematical model. An increased likelihood for example may be relative or absolute and may be expressed qualitatively or quantitatively. For instance, an increased likelihood may be determined simply by determining the subject's measured or derived biomarker values for at least two IRS biomarkers and placing the subject in an “increased likelihood” category, based upon previous population studies. The term “likelihood” is also used interchangeably herein with the term “probability”.

In some embodiments, the biomarkers, including IRS biomarkers, are obtained from a biological sample. The term “biological sample” as used herein refers to a sample that may be extracted, untreated, treated, diluted or concentrated from an animal. The biological sample is suitably a biological fluid such as whole blood, serum, plasma, saliva, urine, sweat, ascitic fluid, peritoneal fluid, synovial fluid, amniotic fluid, cerebrospinal fluid, tissue biopsy, and the like. In certain embodiments, the biological sample contains blood, especially peripheral blood, or a fraction or extract thereof. Typically, the biological sample comprises blood cells such as mature, immature or developing leukocytes, including lymphocytes, polymorphonuclear leukocytes, neutrophils, monocytes, reticulocytes, basophils, coelomocytes, hemocytes, eosinophils, megakaryocytes, macrophages, dendritic cells natural killer cells, or fraction of such cells (e.g., a nucleic acid or protein fraction). In specific embodiments, the biological sample comprises leukocytes including peripheral blood mononuclear cells (PBMC). By “obtained from” is meant to come into possession. Biological or reference samples so obtained include, for example, nucleic acid extracts or polypeptide extracts isolated or derived from a particular source. For instance, the extract may be isolated directly from a biological fluid or tissue of a subject.

The term “nucleic acid” or “polynucleotide” as used herein includes RNA, mRNA, miRNA, cRNA, cDNA mtDNA, or DNA. The term typically refers to a polymeric form of nucleotides of at least 10 bases in length, either ribonucleotides or deoxynucleotides or a modified form of either type of nucleotide. The term includes single and double stranded forms of DNA or RNA.

“Protein”, “polypeptide” and “peptide” are used interchangeably herein to refer to a polymer of amino acid residues and to variants and synthetic analogues of the same.

In some embodiments, biomarker signatures are determined through analysis of measured or derived IRS biomarker values for IRS biomarkers of one or more control subjects that have or do not have a condition. These biomarkers are referred to herein as “reference IRS biomarkers”. In specific examples, individual control subjects are selected from “healthy control subjects”, “non-healthy control subjects”, “SIRS control subjects”, “inSIRS control subjects”, “ipSIRS control subjects”, “control subjects with a particular stage of ipSIRS”, illustrative examples of which include “mild sepsis control subjects”, “severe sepsis control subjects” and “septic shock control subjects”, etc.), which are also referred to herein as control groups (e.g., “healthy control group”, “non-healthy control group”, “SIRS control group”, “inSIRS control group”, “ipSIRS control group”, “ipSIRS stage group”, illustrative examples of which include “mild sepsis control group”, “severe sepsis control group”, and “septic shock control group”, etc.).

Suitably, an individual measured or derived IRS biomarker value corresponds to the level or amount of a respective IRS biomarker or to a function that is applied to that level or amount. As used herein the terms “level” and “amount” are used interchangeably herein to refer to a quantitative amount (e.g., weight or moles), a semi-quantitative amount, a relative amount (e.g., weight % or mole % within class), a concentration, and the like. Thus, these terms encompass absolute or relative amounts or concentrations of IRS biomarkers in a sample.

In some embodiments, the presence, absence, degree or prognosis of at least one condition in a biological subject is established by determining a plurality of IRS biomarker values, wherein each IRS biomarker value is indicative of a value measured or derived for at least one IRS biomarker in a biological sample obtained from the biological subject. These biomarkers are referred to herein as “sample IRS biomarkers”. In accordance with the present invention, a sample IRS biomarker corresponds to a reference IRS biomarker (also referred to herein as a “corresponding IRS biomarker”). By “corresponding IRS biomarker” is meant an IRS biomarker that is structurally and/or functionally similar to a reference IRS biomarker. Representative corresponding IRS biomarkers include expression products of allelic variants (same locus), homologues (different locus), and orthologues (different organism) of reference IRS biomarker genes. Nucleic acid variants of reference IRS biomarker genes and encoded IRS biomarker polynucleotide expression products can contain nucleotide substitutions, deletions, inversions and/or insertions. Variation can occur in either or both the coding and non-coding regions. The variations can produce both conservative and non-conservative amino acid substitutions (as compared in the encoded product). For nucleotide sequences, conservative variants include those sequences that, because of the degeneracy of the genetic code, encode the amino acid sequence of a reference IRS polypeptide.

Generally, variants of a particular IRS biomarker gene or polynucleotide will have at least about 40%, 45%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59% 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69% 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity to that particular nucleotide sequence as determined by sequence alignment programs known in the art using default parameters. In some embodiments, the IRS biomarker gene or polynucleotide displays at least about 40%, 45%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59% 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69% 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity to a nucleotide sequence selected from any one of SEQ ID NO: 1-1650, as summarized in Table 1.

Corresponding IRS biomarkers also include amino acid sequences that display substantial sequence similarity or identity to the amino acid sequence of a reference IRS biomarker polypeptide. In general, an amino acid sequence that corresponds to a reference amino acid sequence will display at least about 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 97, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99% or even up to 100% sequence similarity or identity to a reference amino acid sequence selected from any one of SEQ ID NO: 1651-3284, as summarized in Table 2.

In some embodiments, calculations of sequence similarity or sequence identity between sequences are performed as follows:

To determine the percentage identity of two amino acid sequences, or of two nucleic acid sequences, the sequences are aligned for optimal comparison purposes (e.g., gaps can be introduced in one or both of a first and a second amino acid or nucleic acid sequence for optimal alignment and non-homologous sequences can be disregarded for comparison purposes). In some embodiments, the length of a reference sequence aligned for comparison purposes is at least 30%, usually at least 40%, more usually at least 50%, 60%, and even more usually at least 70%, 80%, 90%, 100% of the length of the reference sequence. The amino acid residues or nucleotides at corresponding amino acid positions or nucleotide positions are then compared. When a position in the first sequence is occupied by the same amino acid residue or nucleotide at the corresponding position in the second sequence, then the molecules are identical at that position. For amino acid sequence comparison, when a position in the first sequence is occupied by the same or similar amino acid residue (i.e., conservative substitution) at the corresponding position in the second sequence, then the molecules are similar at that position.

The percentage identity between the two sequences is a function of the number of identical amino acid residues shared by the sequences at individual positions, taking into account the number of gaps, and the length of each gap, which need to be introduced for optimal alignment of the two sequences. By contrast, the percentage similarity between the two sequences is a function of the number of identical and similar amino acid residues shared by the sequences at individual positions, taking into account the number of gaps, and the length of each gap, which need to be introduced for optimal alignment of the two sequences.

The comparison of sequences and determination of percentage identity or percentage similarity between sequences can be accomplished using a mathematical algorithm. In certain embodiments, the percentage identity or similarity between amino acid sequences is determined using the Needleman and Wunsch, (1970, J. Mol. Biol. 48: 444-453) algorithm which has been incorporated into the GAP program in the GCG software package (available at http://www.gcg.com), using either a Blossum 62 matrix or a PAM250 matrix, and a gap weight of 16, 14, 12, 10, 8, 6, or 4 and a length weight of 1, 2, 3, 4, 5, or 6. In specific embodiments, the percent identity between nucleotide sequences is determined using the GAP program in the GCG software package (available at http://www.gcg.com), using a NWSgapdna. CMP matrix and a gap weight of 40, 50, 60, 70, or 80 and a length weight of 1, 2, 3, 4, 5, or 6. An non-limiting set of parameters (and the one that should be used unless otherwise specified) includes a Blossum 62 scoring matrix with a gap penalty of 12, a gap extend penalty of 4, and a frameshift gap penalty of 5.

In some embodiments, the percentage identity or similarity between amino acid or nucleotide sequences can be determined using the algorithm of E. Meyers and W. Miller (1989, Cabios, 4: 11-17) which has been incorporated into the ALIGN program (version 2.0), using a PAM120 weight residue table, a gap length penalty of 12 and a gap penalty of 4.

The nucleic acid and protein sequences described herein can be used as a “query sequence” to perform a search against public databases to, for example, identify other family members or related sequences. Such searches can be performed using the NBLAST and XBLAST programs (version 2.0) of Altschul, et al., (1990, J Mol Biol., 215: 403-10). BLAST nucleotide searches can be performed with the NBLAST program, score=100, wordlength=12 to obtain nucleotide sequences homologous to 53010 nucleic acid molecules of the invention. BLAST protein searches can be performed with the XBLAST program, score=50, wordlength=3 to obtain amino acid sequences homologous to YYYYY protein molecules of the invention. To obtain gapped alignments for comparison purposes, Gapped BLAST can be utilized as described in Altschul et al., (1997, Nucleic Acids Res, 25: 3389-3402). When utilizing BLAST and Gapped BLAST programs, the default parameters of the respective programs (e.g., XBLAST and NBLAST) can be used.

Corresponding IRS biomarker polynucleotides also include nucleic acid sequences that hybridize to reference IRS biomarker polynucleotides, or to their complements, under stringency conditions described below. As used herein, the term “hybridizes under low stringency, medium stringency, high stringency, or very high stringency conditions” describes conditions for hybridization and washing. “Hybridization” is used herein to denote the pairing of complementary nucleotide sequences to produce a DNA-DNA hybrid or a DNA-RNA hybrid. Complementary base sequences are those sequences that are related by the base-pairing rules. In DNA, A pairs with T and C pairs with G. In RNA, U pairs with A and C pairs with G. In this regard, the terms “match” and “mismatch” as used herein refer to the hybridization potential of paired nucleotides in complementary nucleic acid strands. Matched nucleotides hybridize efficiently, such as the classical A-T and G-C base pair mentioned above. Mismatches are other combinations of nucleotides that do not hybridize efficiently.

Guidance for performing hybridization reactions can be found in Ausubel et al., (1998, supra), Sections 6.3.1-6.3.6. Aqueous and non-aqueous methods are described in that reference and either can be used. Reference herein to low stringency conditions include and encompass from at least about 1% v/v to at least about 15% v/v formamide and from at least about 1 M to at least about 2 M salt for hybridization at 42° C., and at least about 1 M to at least about 2 M salt for washing at 42° C. Low stringency conditions also may include 1% Bovine Serum Albumin (BSA), 1 mM EDTA, 0.5 M NaHPO₄ (pH 7.2), 7% SDS for hybridization at 65° C., and (i) 2×SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO₄ (pH 7.2), 5% SDS for washing at room temperature. One embodiment of low stringency conditions includes hybridization in 6× sodium chloride/sodium citrate (SSC) at about 45° C., followed by two washes in 0.2×SSC, 0.1% SDS at least at 50° C. (the temperature of the washes can be increased to 55° C. for low stringency conditions). Medium stringency conditions include and encompass from at least about 16% v/v to at least about 30% v/v formamide and from at least about 0.5 M to at least about 0.9 M salt for hybridization at 42° C., and at least about 0.1 M to at least about 0.2 M salt for washing at 55° C. Medium stringency conditions also may include 1% Bovine Serum Albumin (BSA), 1 mM EDTA, 0.5 M NaHPO₄ (pH 7.2), 7% SDS for hybridization at 65° C., and (i) 2×SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO₄ (pH 7.2), 5% SDS for washing at 60-65° C. One embodiment of medium stringency conditions includes hybridizing in 6×SSC at about 45° C., followed by one or more washes in 0.2×SSC, 0.1% SDS at 60° C. High stringency conditions include and encompass from at least about 31% v/v to at least about 50% v/v formamide and from about 0.01 M to about 0.15 M salt for hybridization at 42° C., and about 0.01 M to about 0.02 M salt for washing at 55° C. High stringency conditions also may include 1% BSA, 1 mM EDTA, 0.5 M NaHPO₄ (pH 7.2), 7% SDS for hybridization at 65° C., and (i) 0.2×SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO₄ (pH 7.2), 1% SDS for washing at a temperature in excess of 65° C. One embodiment of high stringency conditions includes hybridizing in 6×SSC at about 45° C., followed by one or more washes in 0.2×SSC, 0.1% SDS at 65° C.

In certain embodiments, a corresponding IRS biomarker polynucleotide is one that hybridizes to a disclosed nucleotide sequence under very high stringency conditions. One embodiment of very high stringency conditions includes hybridizing 0.5 M sodium phosphate, 7% SDS at 65° C., followed by one or more washes at 0.2×SSC, 1% SDS at 65° C.

Other stringency conditions are well known in the art and a skilled addressee will recognize that various factors can be manipulated to optimize the specificity of the hybridization. Optimization of the stringency of the final washes can serve to ensure a high degree of hybridization. For detailed examples, see Ausubel et al., supra at pages 2.10.1 to 2.10.16 and Sambrook et al. (1989, supra) at sections 1.101 to 1.104.

The IRS biomarkers disclosed herein each have significant sensitivity and specificity for diagnosing the presence, absence or degree of at least condition selected from a healthy condition (e.g., a normal condition or one in which inSIRS and inSIRS are absent), inSIRS, ipSIRS or a stage of ipSIRS (e.g., a stage of ipSIRS with a particular severity such as mild sepsis, severe sepsis and septic shock). Accordingly, it is feasible to use individual IRS biomarkers in methods, apparatus and kits that do not rely on the use of low mutual correlation between biomarkers to diagnose the presence, absence or degree of the at least condition. In illustrative examples of this type, the invention contemplates methods, kits and apparatus that are useful for diagnosing that inSIRS or a healthy condition is present or absent in a biological subject, which suitably involve: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from inSIRS and a healthy condition, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature of a sample from a subject, wherein the sample biomarker signature evaluates for an individual IRS biomarker in the reference biomarker signature a corresponding IRS biomarker; and (3) diagnosing the presence or absence of the condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein an individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group A and group B IRS biomarker genes as defined herein.

In other non-limiting examples, the methods, apparatus and kits are useful for diagnosing that ipSIRS or a healthy condition is present or absent in the biological subject, which suitably involve: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from ipSIRS and a healthy condition, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature of a sample from a subject, wherein the sample biomarker signature evaluates for an individual IRS biomarker in the reference biomarker signature a corresponding IRS biomarker; and (3) diagnosing the presence or absence of the condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein an individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group C and group D IRS biomarker genes as defined herein.

In still other non-limiting examples, the methods, apparatus and kits are useful for diagnosing that inSIRS or ipSIRS is present or absent in the biological subject, which suitably involve: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from inSIRS and ipSIRS, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature of a sample from a subject, wherein the sample biomarker signature evaluates for an individual IRS biomarker in the reference biomarker signature a corresponding IRS biomarker; and (3) diagnosing the presence or absence of the condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein an individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group E and group F IRS biomarker genes as defined herein.

In still other illustrative examples, the methods, apparatus and kits are useful for diagnosing that mild sepsis or severe sepsis is present or absent in the biological subject, which suitably involve: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from mild sepsis and severe sepsis, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature of a sample from a subject, wherein the sample biomarker signature evaluates for an individual IRS biomarker in the reference biomarker signature a corresponding IRS biomarker; and (3) diagnosing the presence or absence of the condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein an individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group K and group L IRS biomarker genes as defined herein.

In still other illustrative examples, the methods, apparatus and kits are useful for diagnosing that mild sepsis or septic shock is present or absent in the biological subject, which suitably involve: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from mild sepsis and septic shock, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature of a sample from a subject, wherein the sample biomarker signature evaluates for an individual IRS biomarker in the reference biomarker signature a corresponding IRS biomarker; and (3) diagnosing the presence or absence of the condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein an individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group M and group N IRS biomarker genes as defined herein.

In other non-limiting examples, the methods, apparatus and kits are useful for diagnosing that severe sepsis or septic shock is present or absent in the biological subject, which suitably involve: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from severe sepsis and septic shock, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature of a sample from a subject, wherein the sample biomarker signature evaluates for an individual IRS biomarker in the reference biomarker signature a corresponding IRS biomarker; and (3) diagnosing the presence or absence of the condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein an individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group O and group P IRS biomarker genes as defined herein.

The biomarkers may be quantified or detected using any suitable technique. In specific embodiments, the biomarkers, including the IRS biomarkers, are quantified using reagents that determine the level or abundance of individual biomarkers. Non-limiting reagents of this type include reagents for use in nucleic acid- and protein-based assays.

In illustrative nucleic acid-based assays, nucleic acid is isolated from cells contained in the biological sample according to standard methodologies (Sambrook, et al., 1989, supra; and Ausubel et al., 1994, supra). The nucleic acid is typically fractionated (e.g., poly A⁺ RNA) or whole cell RNA. Where RNA is used as the subject of detection, it may be desired to convert the RNA to a complementary DNA. In some embodiments, the nucleic acid is amplified by a template-dependent nucleic acid amplification technique. A number of template dependent processes are available to amplify the IRS biomarker sequences present in a given template sample. An exemplary nucleic acid amplification technique is the polymerase chain reaction (referred to as PCR), which is described in detail in U.S. Pat. Nos. 4,683,195, 4,683,202 and 4,800,159, Ausubel et al. (supra), and in Innis et al., (“PCR Protocols”, Academic Press, Inc., San Diego Calif., 1990). Briefly, in PCR, two primer sequences are prepared that are complementary to regions on opposite complementary strands of the biomarker sequence. An excess of deoxynucleotide triphosphates are added to a reaction mixture along with a DNA polymerase, e.g., Taq polymerase. If a cognate IRS biomarker sequence is present in a sample, the primers will bind to the biomarker and the polymerase will cause the primers to be extended along the biomarker sequence by adding on nucleotides. By raising and lowering the temperature of the reaction mixture, the extended primers will dissociate from the biomarker to form reaction products, excess primers will bind to the biomarker and to the reaction products and the process is repeated. A reverse transcriptase PCR amplification procedure may be performed in order to quantify the amount of mRNA amplified. Methods of reverse transcribing RNA into cDNA are well known and described in Sambrook et al., 1989, supra. Alternative methods for reverse transcription utilize thermostable, RNA-dependent DNA polymerases. These methods are described in WO 90/07641. Polymerase chain reaction methodologies are well known in the art.

In certain advantageous embodiments, the template-dependent amplification involves quantification of transcripts in real-time. For example, RNA or DNA may be quantified using the Real-Time PCR technique (Higuchi, 1992, et al., Biotechnology 10: 413-417). By determining the concentration of the amplified products of the target DNA in PCR reactions that have completed the same number of cycles and are in their linear ranges, it is possible to determine the relative concentrations of the specific target sequence in the original DNA mixture. If the DNA mixtures are cDNAs synthesized from RNAs isolated from different tissues or cells, the relative abundance of the specific mRNA from which the target sequence was derived can be determined for the respective tissues or cells. This direct proportionality between the concentration of the PCR products and the relative mRNA abundance is only true in the linear range of the PCR reaction. The final concentration of the target DNA in the plateau portion of the curve is determined by the availability of reagents in the reaction mix and is independent of the original concentration of target DNA. In specific embodiments, multiplexed, tandem PCR (MT-PCR) is employed, which uses a two-step process for gene expression profiling from small quantities of RNA or DNA, as described for example in US Pat. Appl. Pub. No. 20070190540. In the first step, RNA is converted into cDNA and amplified using multiplexed gene specific primers. In the second step each individual gene is quantitated by real time PCR.

In certain embodiments, target nucleic acids are quantified using blotting techniques, which are well known to those of skill in the art. Southern blotting involves the use of DNA as a target, whereas Northern blotting involves the use of RNA as a target. Each provides different types of information, although cDNA blotting is analogous, in many aspects, to blotting or RNA species. Briefly, a probe is used to target a DNA or RNA species that has been immobilized on a suitable matrix, often a filter of nitrocellulose. The different species should be spatially separated to facilitate analysis. This often is accomplished by gel electrophoresis of nucleic acid species followed by “blotting” on to the filter. Subsequently, the blotted target is incubated with a probe (usually labeled) under conditions that promote denaturation and rehybridization. Because the probe is designed to base pair with the target, the probe will bind a portion of the target sequence under renaturing conditions. Unbound probe is then removed, and detection is accomplished as described above. Following detection/quantification, one may compare the results seen in a given subject with a control reaction or a statistically significant reference group or population of control subjects as defined herein. In this way, it is possible to correlate the amount of an IRS biomarker nucleic acid detected with the progression or severity of the disease. As used herein, the term “probe” refers to a molecule that binds to a specific sequence or sub-sequence or other moiety of another molecule. Unless otherwise indicated, the term “probe” typically refers to a nucleic acid probe that binds to another nucleic acid, also referred to herein as a “target polynucleotide”, through complementary base pairing. Probes can bind target polynucleotides lacking complete sequence complementarity with the probe, depending on the stringency of the hybridization conditions. Probes can be labeled directly or indirectly and include primers within their scope. By “primer” is meant an oligonucleotide which, when paired with a strand of DNA, is capable of initiating the synthesis of a primer extension product in the presence of a suitable polymerizing agent. The primer is preferably single-stranded for maximum efficiency in amplification but can alternatively be double-stranded. A primer must be sufficiently long to prime the synthesis of extension products in the presence of the polymerization agent. The length of the primer depends on many factors, including application, temperature to be employed, template reaction conditions, other reagents, and source of primers. For example, depending on the complexity of the target sequence, the primer may be at least about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 50, 75, 100, 150, 200, 300, 400, 500, to one base shorter in length than the template sequence at the 3′ end of the primer to allow extension of a nucleic acid chain, though the 5′ end of the primer may extend in length beyond the 3′ end of the template sequence. In certain embodiments, primers can be large polynucleotides, such as from about 35 nucleotides to several kilobases or more. Primers can be selected to be “substantially complementary” to the sequence on the template to which it is designed to hybridize and serve as a site for the initiation of synthesis. By “substantially complementary”, it is meant that the primer is sufficiently complementary to hybridize with a target polynucleotide. Desirably, the primer contains no mismatches with the template to which it is designed to hybridize but this is not essential. For example, non-complementary nucleotide residues can be attached to the 5′ end of the primer, with the remainder of the primer sequence being complementary to the template. Alternatively, non-complementary nucleotide residues or a stretch of non-complementary nucleotide residues can be interspersed into a primer, provided that the primer sequence has sufficient complementarity with the sequence of the template to hybridize therewith and thereby form a template for synthesis of the extension product of the primer.

Also contemplated are biochip-based technologies such as those described by Hacia et al. (1996, Nature Genetics 14: 441-447) and Shoemaker et al. (1996, Nature Genetics 14: 450-456). Briefly, these techniques involve quantitative methods for analyzing large numbers of genes rapidly and accurately. By tagging genes with oligonucleotides or using fixed nucleic acid probe arrays, one can employ biochip technology to segregate target molecules as high-density arrays and screen these molecules on the basis of hybridization. See also Pease et al. (1994, Proc. Natl. Acad. Sci. U.S.A. 91: 5022-5026); Fodor et al. (1991, Science 251: 767-773). Briefly, nucleic acid probes to IRS biomarker polynucleotides are made and attached to biochips to be used in screening and diagnostic methods, as outlined herein. The nucleic acid probes attached to the biochip are designed to be substantially complementary to specific expressed IRS biomarker nucleic acids, i.e., the target sequence (either the target sequence of the sample or to other probe sequences, for example in sandwich assays), such that hybridization of the target sequence and the probes of the present invention occur. This complementarity need not be perfect; there may be any number of base pair mismatches, which will interfere with hybridization between the target sequence and the nucleic acid probes of the present invention. However, if the number of mismatches is so great that no hybridization can occur under even the least stringent of hybridization conditions, the sequence is not a complementary target sequence. In certain embodiments, more than one probe per sequence is used, with either overlapping probes or probes to different sections of the target being used. That is, two, three, four or more probes, with three being desirable, are used to build in a redundancy for a particular target. The probes can be overlapping (i.e. have some sequence in common), or separate.

In an illustrative biochip analysis, oligonucleotide probes on the biochip are exposed to or contacted with a nucleic acid sample suspected of containing one or more IRS biomarker polynucleotides under conditions favoring specific hybridization. Sample extracts of DNA or RNA, either single or double-stranded, may be prepared from fluid suspensions of biological materials, or by grinding biological materials, or following a cell lysis step which includes, but is not limited to, lysis effected by treatment with SDS (or other detergents), osmotic shock, guanidinium isothiocyanate and lysozyme. Suitable DNA, which may be used in the method of the invention, includes cDNA. Such DNA may be prepared by any one of a number of commonly used protocols as for example described in Ausubel, et al., 1994, supra, and Sambrook, et al., 1989, supra.

Suitable RNA, which may be used in the method of the invention, includes messenger RNA, complementary RNA transcribed from DNA (cRNA) or genomic or subgenomic RNA. Such RNA may be prepared using standard protocols as for example described in the relevant sections of Ausubel, et al. 1994, supra and Sambrook, et al. 1989, supra).

cDNA may be fragmented, for example, by sonication or by treatment with restriction endonucleases. Suitably, cDNA is fragmented such that resultant DNA fragments are of a length greater than the length of the immobilized oligonucleotide probe(s) but small enough to allow rapid access thereto under suitable hybridization conditions. Alternatively, fragments of cDNA may be selected and amplified using a suitable nucleotide amplification technique, as described for example above, involving appropriate random or specific primers.

Usually the target IRS biomarker polynucleotides are detectably labeled so that their hybridization to individual probes can be determined. The target polynucleotides are typically detectably labeled with a reporter molecule illustrative examples of which include chromogens, catalysts, enzymes, fluorochromes, chemiluminescent molecules, bioluminescent molecules, lanthanide ions (e.g., Eu³⁴), a radioisotope and a direct visual label. In the case of a direct visual label, use may be made of a colloidal metallic or non-metallic particle, a dye particle, an enzyme or a substrate, an organic polymer, a latex particle, a liposome, or other vesicle containing a signal producing substance and the like. Illustrative labels of this type include large colloids, for example, metal colloids such as those from gold, selenium, silver, tin and titanium oxide. In some embodiments in which an enzyme is used as a direct visual label, biotinylated bases are incorporated into a target polynucleotide.

The hybrid-forming step can be performed under suitable conditions for hybridizing oligonucleotide probes to test nucleic acid including DNA or RNA. In this regard, reference may be made, for example, to NUCLEIC ACID HYBRIDIZATION, A PRACTICAL APPROACH (Homes and Higgins, eds.) (IRL press, Washington D.C., 1985). In general, whether hybridization takes place is influenced by the length of the oligonucleotide probe and the polynucleotide sequence under test, the pH, the temperature, the concentration of mono- and divalent cations, the proportion of G and C nucleotides in the hybrid-forming region, the viscosity of the medium and the possible presence of denaturants. Such variables also influence the time required for hybridization. The preferred conditions will therefore depend upon the particular application. Such empirical conditions, however, can be routinely determined without undue experimentation.

After the hybrid-forming step, the probes are washed to remove any unbound nucleic acid with a hybridization buffer. This washing step leaves only bound target polynucleotides. The probes are then examined to identify which probes have hybridized to a target polynucleotide.

The hybridization reactions are then detected to determine which of the probes has hybridized to a corresponding target sequence. Depending on the nature of the reporter molecule associated with a target polynucleotide, a signal may be instrumentally detected by irradiating a fluorescent label with light and detecting fluorescence in a fluorimeter; by providing for an enzyme system to produce a dye which could be detected using a spectrophotometer; or detection of a dye particle or a colored colloidal metallic or non-metallic particle using a reflectometer; in the case of using a radioactive label or chemiluminescent molecule employing a radiation counter or autoradiography. Accordingly, a detection means may be adapted to detect or scan light associated with the label which light may include fluorescent, luminescent, focused beam or laser light. In such a case, a charge couple device (CCD) or a photocell can be used to scan for emission of light from a probe:target polynucleotide hybrid from each location in the micro-array and record the data directly in a digital computer. In some cases, electronic detection of the signal may not be necessary. For example, with enzymatically generated color spots associated with nucleic acid array format, visual examination of the array will allow interpretation of the pattern on the array. In the case of a nucleic acid array, the detection means is suitably interfaced with pattern recognition software to convert the pattern of signals from the array into a plain language genetic profile. In certain embodiments, oligonucleotide probes specific for different IRS biomarker polynucleotides are in the form of a nucleic acid array and detection of a signal generated from a reporter molecule on the array is performed using a ‘chip reader’. A detection system that can be used by a ‘chip reader’ is described for example by Pirrung et al. (U.S. Pat. No. 5,143,854). The chip reader will typically also incorporate some signal processing to determine whether the signal at a particular array position or feature is a true positive or maybe a spurious signal. Exemplary chip readers are described for example by Fodor et al. (U.S. Pat. No. 5,925,525). Alternatively, when the array is made using a mixture of individually addressable kinds of labeled microbeads, the reaction may be detected using flow cytometry.

In certain embodiments, the IRS biomarker is a target RNA (e.g., mRNA) or a DNA copy of the target RNA whose level is measured using at least one nucleic acid probe that hybridizes under at least low, medium, or high stringency conditions to the target RNA or to the DNA copy, wherein the nucleic acid probe comprises at least 15 (e.g., 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or more) contiguous nucleotides of an IRS biomarker polynucleotide. In some embodiments, the measured level or abundance of the target RNA or its DNA copy is normalized to the level or abundance of a reference RNA or a DNA copy of the reference RNA. Suitably, the nucleic acid probe is immobilized on a solid or semi-solid support. In illustrative examples of this type, the nucleic acid probe forms part of a spatial array of nucleic acid probes. In some embodiments, the level of nucleic acid probe that is bound to the target RNA or to the DNA copy is measured by hybridization (e.g., using a nucleic acid array). In other embodiments, the level of nucleic acid probe that is bound to the target RNA or to the DNA copy is measured by nucleic acid amplification (e.g., using a polymerase chain reaction (PCR)). In still other embodiments, the level of nucleic acid probe that is bound to the target RNA or to the DNA copy is measured by nuclease protection assay.

In other embodiments, IRS biomarker protein levels are assayed using protein-based assays known in the art. For example, when an IRS biomarker protein is an enzyme, the protein can be quantified based upon its catalytic activity or based upon the number of molecules of the protein contained in a sample. Antibody-based techniques may be employed including, for example, immunoassays, such as the enzyme-linked immunosorbent assay (ELISA) and the radioimmunoassay (RIA).

In specific embodiments, protein-capture arrays that permit simultaneous detection and/or quantification of a large number of proteins are employed. For example, low-density protein arrays on filter membranes, such as the universal protein array system (Ge, 2000 Nucleic Acids Res. 28(2):e3) allow imaging of arrayed antigens using standard ELISA techniques and a scanning charge-coupled device (CCD) detector Immuno-sensor arrays have also been developed that enable the simultaneous detection of clinical analytes. It is now possible using protein arrays, to profile protein expression in bodily fluids, such as in sera of healthy or diseased subjects, as well as in subjects pre- and post-drug treatment.

Exemplary protein capture arrays include arrays comprising spatially addressed antigen-binding molecules, commonly referred to as antibody arrays, which can facilitate extensive parallel analysis of numerous proteins defining a proteome or subproteome. Antibody arrays have been shown to have the required properties of specificity and acceptable background, and some are available commercially (e.g., BD Biosciences, Clontech, Bio-Rad and Sigma). Various methods for the preparation of antibody arrays have been reported (see, e.g., Lopez et al., 2003 J. Chromatogram. B 787:19-27; Cahill, 2000 Trends in Biotechnology 7:47-51; U.S. Pat. App. Pub. 2002/0055186; U.S. Pat. App. Pub. 2003/0003599; PCT publication WO 03/062444; PCT publication WO 03/077851; PCT publication WO 02/59601; PCT publication WO 02/39120; PCT publication WO 01/79849; PCT publication WO 99/39210). The antigen-binding molecules of such arrays may recognize at least a subset of proteins expressed by a cell or population of cells, illustrative examples of which include growth factor receptors, hormone receptors, neurotransmitter receptors, catecholamine receptors, amino acid derivative receptors, cytokine receptors, extracellular matrix receptors, antibodies, lectins, cytokines, serpins, proteases, kinases, phosphatases, ras-like GTPases, hydrolases, steroid hormone receptors, transcription factors, heat-shock transcription factors, DNA-binding proteins, zinc-finger proteins, leucine-zipper proteins, homeodomain proteins, intracellular signal transduction modulators and effectors, apoptosis-related factors, DNA synthesis factors, DNA repair factors, DNA recombination factors and cell-surface antigens.

Individual spatially distinct protein-capture agents are typically attached to a support surface, which is generally planar or contoured. Common physical supports include glass slides, silicon, microwells, nitrocellulose or PVDF membranes, and magnetic and other microbeads.

Particles in suspension can also be used as the basis of arrays, providing they are coded for identification; systems include color coding for microbeads (e.g., available from Luminex, Bio-Rad and Nanomics Biosystems) and semiconductor nanocrystals (e.g., QDots™, available from Quantum Dots), and barcoding for beads (UltraPlex™, available from Smartbeads) and multimetal microrods (Nanobarcodes™ particles, available from Surromed). Beads can also be assembled into planar arrays on semiconductor chips (e.g., available from LEAPS technology and BioArray Solutions). Where particles are used, individual protein-capture agents are typically attached to an individual particle to provide the spatial definition or separation of the array. The particles may then be assayed separately, but in parallel, in a compartmentalized way, for example in the wells of a microtiter plate or in separate test tubes.

In operation, a protein sample, which is optionally fragmented to form peptide fragments (see, e.g., U.S. Pat. App. Pub. 2002/0055186), is delivered to a protein-capture array under conditions suitable for protein or peptide binding, and the array is washed to remove unbound or non-specifically bound components of the sample from the array. Next, the presence or amount of protein or peptide bound to each feature of the array is detected using a suitable detection system. The amount of protein bound to a feature of the array may be determined relative to the amount of a second protein bound to a second feature of the array. In certain embodiments, the amount of the second protein in the sample is already known or known to be invariant.

In specific embodiments, the IRS biomarker is a target polypeptide whose level is measured using at least one antigen-binding molecule that is immuno-interactive with the target polypeptide. In these embodiments, the measured level of the target polypeptide is normalized to the level of a reference polypeptide. Suitably, the antigen-binding molecule is immobilized on a solid or semi-solid support. In illustrative examples of this type, the antigen-binding molecule forms part of a spatial array of antigen-binding molecule. In some embodiments, the level of antigen-binding molecule that is bound to the target polypeptide is measured by immunoassay (e.g., using an ELISA). Reference herein to “immuno-interactive” includes reference to any interaction, reaction, or other form of association between molecules and in particular where one of the molecules is, or mimics, a component of the immune system.

All the essential reagents required for detecting and quantifying the biomarkers of the invention, including IRS biomarkers, may be assembled together in a kit. In some embodiments, the kit comprises: (i) a reagent that allows quantification (e.g., determining the level or abundance) of a first biomarker; and (ii) a reagent that allows quantification (e.g., determining the level or abundance) of a second biomarker, wherein the first and second biomarkers have a mutual correlation in respect of at least one condition (e.g., at least one of a healthy condition and one or more diseases such as but not limited to inSIRS, ipSIRS or a stage of ipSIRS (e.g., a stage of ipSIRS with a particular severity such as mild sepsis, severe sepsis and septic shock)) that lies within a mutual correlation range of between ±0.9, and wherein a combination of respective biomarker values for the first and second biomarkers that are measured for or derived from a biological subject has a performance value greater than or equal to a performance threshold representing the ability of the combination of the first and second biomarkers to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being a variance explained of at least 0.3. In some embodiments, the kit further comprises (iii) a reagent that allows quantification (e.g., determining the level or abundance) of a third biomarker; and (iv) a reagent that allows quantification (e.g., determining the level or abundance) of a fourth biomarker, wherein the third and fourth biomarkers have a mutual correlation in respect of at the least one condition that lies within a mutual correlation range of between ±0.9, and wherein a combination of respective biomarker values for the third and fourth biomarkers that are measured for or derived from a biological subject has a performance value greater than or equal to a performance threshold representing the ability of the combination of the third and fourth biomarkers to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being a variance explained of at least 0.3.

In advantageous embodiments, the kits of the present invention are useful for diagnosing the presence, absence or degree of at least one condition, or for providing a prognosis for at least one condition, wherein the at least one condition is selected from a healthy condition, inSIRS, ipSIRS or a stage of ipSIRS. In these embodiments, IRS biomarkers are suitably selected from a group as broadly described above and elsewhere herein.

In the context of the present invention, “kit” is understood to mean a product containing the different reagents necessary for carrying out the methods of the invention packed so as to allow their transport and storage. Materials suitable for packing the components of the kit include crystal, plastic (polyethylene, polypropylene, polycarbonate and the like), bottles, vials, paper, envelopes and the like. Additionally, the kits of the invention can contain instructions for the simultaneous, sequential or separate use of the different components contained in the kit. The instructions can be in the form of printed material or in the form of an electronic support capable of storing instructions such that they can be read by a subject, such as electronic storage media (magnetic disks, tapes and the like), optical media (CD-ROM, DVD) and the like. Alternatively or in addition, the media can contain Internet addresses that provide the instructions.

A “reagent that allows quantification of a biomarker” means a compound or material, or set of compounds or materials, which allow quantification of the biomarker. In specific embodiments, the compound, material or set of compounds or materials permit determining the expression level of a gene (e.g., an IRS biomarker gene), including without limitation the extraction of RNA material, the determination of the level of a corresponding RNA, etc., primers for the synthesis of a corresponding cDNA, primers for amplification of DNA, and/or probes capable of specifically hybridizing with the RNAs (or the corresponding cDNAs) encoded by the genes, TaqMan probes, etc.

The kits may also optionally include appropriate reagents for detection of labels, positive and negative controls, washing solutions, blotting membranes, microtiter plates, dilution buffers and the like. For example, a nucleic acid-based detection kit may include (i) a biomarker polynucleotide (e.g., an IRS biomarker polynucleotide) (which may be used as a positive control), (ii) a primer or probe that specifically hybridizes to a biomarker polynucleotide (e.g., an IRS biomarker polynucleotide). Also included may be enzymes suitable for amplifying nucleic acids including various polymerases (reverse transcriptase, Taq, Sequenase™, DNA ligase etc. depending on the nucleic acid amplification technique employed), deoxynucleotides and buffers to provide the necessary reaction mixture for amplification. Such kits also generally will comprise, in suitable means, distinct containers for each individual reagent and enzyme as well as for each primer or probe. Alternatively, a protein-based detection kit may include (i) a biomarker polypeptide (e.g., an IRS biomarker polypeptide) (which may be used as a positive control), (ii) an antibody that binds specifically to a biomarker polypeptide (e.g., an IRS biomarker polypeptide). The kit can also feature various devices (e.g., one or more) and reagents (e.g., one or more) for performing one of the assays described herein; and/or printed instructions for using the kit to quantify the expression of a biomarker gene (e.g., an IRS biomarker gene).

The reagents described herein, which may be optionally associated with detectable labels, can be presented in the format of a microfluidics card, a chip or chamber, a microarray or a kit adapted for use with the assays described in the examples or below, e.g., RT-PCR or Q PCR techniques described herein. The term “microarray” refers to an arrangement of hybridizable array elements, e.g., probes (including primers), ligands, biomarker nucleic acid sequence or protein sequences on a substrate.

The reagents also have utility in compositions for detecting and quantifying the biomarkers of the invention. For example, a reverse transcriptase may be used to reverse transcribe RNA transcripts, including mRNA, in a nucleic acid sample, to produce reverse transcribed transcripts, including reverse transcribed mRNA (also referred to as “cDNA”). The nucleic acid sample is suitably derived from components of the immune system, representative examples of which include components of the innate and adaptive immune systems as broadly discussed for example above. In specific embodiments, the reverse transcribed RNA is derived blood cells (e.g., peripheral blood cells). Suitably, the reverse transcribed RNA is derived leukocytes.

The reagents are suitably used to quantify the reverse transcribed transcripts. For example, oligonucleotide primers that hybridize to the reverse transcribed transcript can be used to amplify at least a portion of the reverse transcribed transcript via a suitable nucleic acid amplification technique, e.g., RT-PCR or Q PCR techniques described herein. Alternatively, oligonucleotide probes may be used to hybridize to the reverse transcribed transcript for the quantification, using a nucleic acid hybridization analysis technique (e.g., microarray analysis), as described for example above. Thus, in some embodiments, a respective oligonucleotide primer or probe is hybridized to a complementary nucleic acid sequence of a reverse transcribed transcript in the compositions of the invention. The compositions typically comprise labeled reagents for detecting and/or quantifying the reverse transcribed transcripts. Representative reagents of this type include labeled oligonucleotide primers or probes that hybridize to RNA transcripts or reverse transcribed RNA, labeled RNA, labeled reverse transcribed RNA as well as labeled oligonucleotide linkers or tags (e.g., a labeled RNA or DNA linker or tag) for labeling (e.g., end labeling such as 3′ end labeling) RNA or reverse transcribed RNA. The primers, probes, RNA or reverse transcribed RNA (i.e., cDNA) (whether labeled or non-labeled) may be immobilized or free in solution. Representative reagents of this type include labeled oligonucleotide primers or probes that hybridize to reverse transcribed and transcripts as well as labeled reverse transcribed transcripts. The label can be any reporter molecule as known in the art, illustrative examples of which are described above and elsewhere herein.

The present invention also encompasses non-reverse transcribed RNA embodiments in which cDNA is not made and the RNA transcripts are directly the subject of the analysis. Thus, in other embodiments, reagents are suitably used to quantify RNA transcripts directly. For example, oligonucleotide probes can be used to hybridize to transcripts for quantification of immune system biomarkers of the invention, using a nucleic acid hybridization analysis technique (e.g., microarray analysis), as described for example above. Thus, in some embodiments, a respective oligonucleotide probe is hybridized to a complementary nucleic acid sequence of an immune system biomarker transcript in the compositions of the invention. In illustrative examples of this type, the compositions may comprise labeled reagents that hybridize to transcripts for detecting and/or quantifying the transcripts. Representative reagents of this type include labeled oligonucleotide probes that hybridize to transcripts as well as labeled transcripts. The primers or probes may be immobilized or free in solution.

The term “immobilized” means that a molecular species of interest is fixed to a solid support, suitably by covalent linkage. This covalent linkage can be achieved by different means depending on the molecular nature of the molecular species. Moreover, the molecular species may be also fixed on the solid support by electrostatic forces, hydrophobic or hydrophilic interactions or Van-der-Waals forces. The above described physico-chemical interactions typically occur in interactions between molecules. In particular embodiments, all that is required is that the molecules (e.g., nucleic acids or polypeptides) remain immobilized or attached to a support under conditions in which it is intended to use the support, for example in applications requiring nucleic acid amplification and/or sequencing or in antibody-binding assays. For example, oligonucleotides or primers are immobilized such that a 3′ end is available for enzymatic extension and/or at least a portion of the sequence is capable of hybridizing to a complementary sequence. In some embodiments, immobilization can occur via hybridization to a surface attached primer, in which case the immobilized primer or oligonucleotide may be in the 3′-5′ orientation. In other embodiments, immobilization can occur by means other than base-pairing hybridization, such as the covalent attachment.

The term “solid support” as used herein refers to a solid inert surface or body to which a molecular species, such as a nucleic acid and polypeptides can be immobilized. Non-limiting examples of solid supports include glass surfaces, plastic surfaces, latex, dextran, polystyrene surfaces, polypropylene surfaces, polyacrylamide gels, gold surfaces, and silicon wafers. In some embodiments, the solid supports are in the form of membranes, chips or particles. For example, the solid support may be a glass surface (e.g., a planar surface of a flow cell channel). In some embodiments, the solid support may comprise an inert substrate or matrix which has been “functionalized”, such as by applying a layer or coating of an intermediate material comprising reactive groups which permit covalent attachment to molecules such as polynucleotides. By way of non-limiting example, such supports can include polyacrylamide hydrogels supported on an inert substrate such as glass. The molecules (e.g., polynucleotides) can be directly covalently attached to the intermediate material (e.g., a hydrogel) but the intermediate material can itself be non-covalently attached to the substrate or matrix (e.g., a glass substrate). The support can include a plurality of particles or beads each having a different attached molecular species.

The present invention also extends to the management of inSIRS, ipSIRS or particular stages of ipSIRS, or prevention of further progression of inSIRS, ipSIRS or particular stages of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), or assessment of the efficacy of therapies in subjects following positive diagnosis for the presence of inSIRS, ipSIRS or particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock) in a subject. The management of inSIRS or ipSIRS conditions is generally highly intensive and can include identification and amelioration of the underlying cause and aggressive use of therapeutic compounds such as, vasoactive compounds, antibiotics, steroids, antibodies to endotoxin, anti-tumor necrosis factor agents, recombinant protein C. In addition, palliative therapies as described for example in Cohen and Glauser (1991, Lancet 338: 736-739) aimed at restoring and protecting organ function can be used such as intravenous fluids and oxygen and tight glycemic control. Therapies for ipSIRS are reviewed in Healy (2002, Ann Pharmacother. 36(4): 648-54) and Brindley (2005, CJEM. 7(4): 227) and Jenkins (2006, J Hosp Med. 1(5): 285-295).

Typically, the therapeutic agents will be administered in pharmaceutical (or veterinary) compositions together with a pharmaceutically acceptable carrier and in an effective amount to achieve their intended purpose. The dose of active compounds administered to a subject should be sufficient to achieve a beneficial response in the subject over time such as a reduction in, or relief from, the symptoms of inSIRS, ipSIRS or particular stages of ipSIRS. The quantity of the pharmaceutically active compounds(s) to be administered may depend on the subject to be treated inclusive of the age, sex, weight and general health condition thereof. In this regard, precise amounts of the active compound(s) for administration will depend on the judgment of the practitioner. In determining the effective amount of the active compound(s) to be administered in the treatment or prevention of inSIRS, ipSIRS or particular stages of ipSIRS, the medical practitioner or veterinarian may evaluate severity of any symptom associated with the presence of inSIRS, ipSIRS or particular stages of ipSIRS including, inflammation, blood pressure anomaly, tachycardia, tachypnea fever, chills, vomiting, diarrhea, skin rash, headaches, confusion, muscle aches, seizures. In any event, those of skill in the art may readily determine suitable dosages of the therapeutic agents and suitable treatment regimens without undue experimentation.

The therapeutic agents may be administered in concert with adjunctive (palliative) therapies to increase oxygen supply to major organs, increase blood flow to major organs and/or to reduce the inflammatory response. Illustrative examples of such adjunctive therapies include non-steroidal-anti-inflammatory drugs (NSAIDs), intravenous saline and oxygen.

In specific embodiments of the present invention, the methods, apparatus and kits described above and elsewhere herein are contemplated for use in methods of treating, preventing or inhibiting the development of at least one condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis or septic shock) in a subject. These methods (also referred to herein as “treatment methods”) generally comprise: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker of a biological subject; (b) determining an indicator using a combination of the plurality of IRS biomarker values, the indicator being at least partially indicative of the presence, absence or degree of the at least one condition, wherein: (i) at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; and (c) administering to the subject, on the basis that the indicator indicates the presence of inSIRS, an effective amount of an agent that treats or ameliorates the symptoms or reverses or inhibits the development of inSIRS, or administering to the subject, on the basis that the indicator indicates the presence of ipSIRS or a particular stage of ipSIRS, an effective amount of an agent that treats or ameliorates the symptoms or reverses or inhibits the development of ipSIRS or the particular stage of ipSIRS.

In advantageous embodiments, the treatment methods comprise: (1) determining a plurality of measured IRS biomarker values, each measured IRS biomarker value being a measured value of an IRS biomarker of the biological subject; and (2) applying a function to at least one of the measured IRS biomarker values to determine at least one derived IRS biomarker value, the at least one derived IRS biomarker value being indicative of a value of a corresponding derived IRS biomarker. The function suitably includes at least one of: (a) multiplying two IRS biomarker values; (b) dividing two IRS biomarker values; (c) adding two IRS biomarker values; (d) subtracting two IRS biomarker values; (e) a weighted sum of at least two IRS biomarker values; (f) a log sum of at least two IRS biomarker values; and (g) a sigmoidal function of at least two IRS biomarker values.

In some embodiments the methods, apparatus and kits of the present invention are used for monitoring, treatment and management of conditions that can lead to inSIRS or ipSIRS, illustrative examples of which include retained placenta, meningitis, endometriosis, shock, toxic shock (i.e., sequelae to tampon use), gastroenteritis, appendicitis, ulcerative colitis, Crohn's disease, inflammatory bowel disease, acid gut syndrome, liver failure and cirrhosis, failure of colostrum transfer in neonates, ischemia (in any organ), bacteremia, infections within body cavities such as the peritoneal, pericardial, thecal, and pleural cavities, burns, severe wounds, excessive exercise or stress, hemodialysis, conditions involving intolerable pain (e.g., pancreatitis, kidney stones), surgical operations, and non-healing lesions. In these embodiments, the methods or kits of the present invention are typically used at a frequency that is effective to monitor the early development of inSIRS, ipSIRS or particular stages of ipSIRS, to thereby enable early therapeutic intervention and treatment of that condition. In illustrative examples, the diagnostic methods or kits are used at least at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 or 24 hour intervals or at least 1, 2, 3, 4, 5 or 6 day intervals, or at least weekly, fortnightly or monthly.

The present invention can be practiced in the field of predictive medicine for the purpose of diagnosis or monitoring the presence or development of a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS in a subject, and/or monitoring response to therapy efficacy.

The biomarker signatures and corresponding indicators of the present invention further enable determination of endpoints in pharmacotranslational studies. For example, clinical trials can take many months or even years to establish the pharmacological parameters for a medicament to be used in treating or preventing inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock). However, these parameters may be associated with a biomarker signature and corresponding indicator of a health state (e.g., a healthy condition). Hence, the clinical trial can be expedited by selecting a treatment regimen (e.g., medicament and pharmaceutical parameters), which results in a biomarker signature associated with a desired health state (e.g., healthy condition). This may be determined for example by: a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker of a biological subject after treatment with a treatment regimen; (b) determining an indicator using a combination of the plurality of IRS biomarker values, the indicator being at least partially indicative of the presence, absence or degree of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, wherein: (i) at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, and (c) determining that the treatment regimen is effective for changing the health status of the subject to the desired health state (e.g., healthy condition) on the basis that the indicator indicates the presence of a healthy condition or the presence of a condition of a lower degree relative to the degree of the condition in the subject before treatment with the treatment regimen. As used herein, the term “degree” refers to the extent or stage of a condition. Thus, for example, mild sepsis is a stage or degree of sepsis that is lower than severe sepsis. Similarly, severe sepsis is a stage or degree of sepsis that is lower than septic shock. Accordingly, this aspect of the present invention advantageously provides methods of monitoring the efficacy of a particular treatment regimen in a subject (for example, in the context of a clinical trial) already diagnosed with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS. These methods take advantage of measured or derived IRS biomarker values that correlate with treatment efficacy to determine, for example, whether measured or derived IRS biomarker values of a subject undergoing treatment partially or completely normalize during the course of or following therapy or otherwise shows changes associated with responsiveness to the therapy.

As used herein, the term “treatment regimen” refers to prophylactic and/or therapeutic (i.e., after onset of a specified condition) treatments, unless the context specifically indicates otherwise. The term “treatment regimen” encompasses natural substances and pharmaceutical agents (i.e., “drugs”) as well as any other treatment regimen including but not limited to dietary treatments, physical therapy or exercise regimens, surgical interventions, and combinations thereof.

Accordingly, the invention provides methods of correlating a biomarker signature with an effective treatment regimen for a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), wherein the methods generally comprise: (a) determining a biomarker signature defining a combination of at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker values corresponding to values of at least two IRS biomarkers that can be measured for or derived from a biological subject having the condition and for whom an effective treatment has been identified, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of the condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the condition, or to provide a prognosis for the condition, the performance threshold being indicative of an explained variance of at least 0.3; and (b) correlating the biomarker signature so determined with an effective treatment regimen for the condition. The term “correlating” generally refers to determining a relationship between one type of data with another or with a state. In specific embodiments, an indicator or biomarker signature is correlated to a global probability or a particular outcome, using receiver operating characteristic (ROC) curves.

The invention further provides methods of determining whether a treatment regimen is effective for treating a subject with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock). These methods generally comprise: (a) determining a plurality of post-treatment IRS biomarker values, each post-treatment IRS biomarker value being indicative of a value measured or derived for at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker of a biological subject after treatment with the treatment regimen; (b) determining a post-treatment indicator using a combination of the plurality of post-treatment IRS biomarker values, the post-treatment indicator being at least partially indicative of the presence, absence or degree of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, wherein: (i) at the least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarkers have a mutual correlation in respect of the at least one condition that lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the post-treatment indicator has a performance value greater than or equal to a performance threshold representing the ability of the post-treatment indicator to diagnose the presence, absence or degree of the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein the post-treatment indicator indicates whether the treatment regimen is effective for treating the condition in the subject on the basis that post-treatment indicator indicates the presence of a healthy condition or the presence of a condition of a lower degree relative to the degree of the condition in the subject before treatment with the treatment regimen.

The invention can also be practiced to evaluate whether a subject is responding (i.e., a positive response) or not responding (i.e., a negative response) to a treatment regimen or has a side effect to a treatment regimen. This aspect of the invention provides methods of correlating a biomarker signature with a positive or negative response or a side effect to a treatment regimen, which generally comprise: (a) determining a biomarker signature defining a combination of at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker values corresponding to values of at least two IRS biomarkers that can be measured for or derived from a biological subject following commencement of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; and (b) correlating the biomarker signature so determined with a positive or negative response to the treatment regimen. As used herein, the term “positive response” means that the result of the treatment regimen includes some clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or a slowing of the progression of the condition. By contrast, the term “negative response” means that the treatment regimen provides no clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or increases the rate of progression of the condition.

The invention also encompasses methods of determining a positive or negative response to a treatment regimen and/or a side effect of a treatment regimen by a subject with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS. These methods generally comprise: (a) correlating a reference biomarker signature with a positive or negative response or a side effect to the treatment regimen, wherein the biomarker signature defines a combination of at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker values corresponding to values of at least two IRS biomarkers that are measured for or derived from a control biological subject or control group, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; (b) determining a sample biomarker signature defining a combination of at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker values corresponding to values of at least two IRS biomarkers that are measured for or derived from a biological subject following commencement of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3; wherein the sample biomarker signature indicates whether the subject is responding positively or negatively to the treatment regimen and/or is developing a side effect from the treatment regimen, based on the correlation of the reference biomarker signature with the positive or negative response or side effect to the treatment regimen.

In related embodiments, the present invention further contemplates methods of determining a positive or negative response to a treatment regimen and/or a side effect to a treatment regimen by a biological subject. These methods generally comprise: (a) determining a sample biomarker signature defining a combination of at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker values corresponding to values of at least two IRS biomarkers that are measured for or derived from a biological subject following commencement of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a mutual correlation in respect of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, which lies within a mutual correlation range, the mutual correlation range being between ±0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or degree of the at least one condition, or to provide a prognosis for the at least one condition, the performance threshold being indicative of an explained variance of at least 0.3, wherein the sample biomarker signature is correlated with a positive or negative response to the treatment regimen and/or to a side effect from the treatment regimen; and (b) determining whether the subject is responding positively or negatively to the treatment regimen and/or is developing a side effect from the treatment regimen based on the sample biomarker signature.

This above methods can be practiced to identify responders or non-responders relatively early in the treatment process, i.e., before clinical manifestations of efficacy. In this way, the treatment regimen can optionally be discontinued, a different treatment protocol can be implemented and/or supplemental therapy can be administered. Thus, in some embodiments, a sample IRS biomarker signature is obtained within about 2 hours, 4 hours, 6 hours, 12 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 1 week, 2 weeks, 3 weeks, 4 weeks, 6 weeks, 8 weeks, 10 weeks, 12 weeks, 4 months, six months or longer of commencing therapy.

A number of non-limiting example signatures for use in diagnosing respective conditions will now be described. For the purpose of illustration, the above-described process was used to select biomarkers that provided the theoretical best diagnostic biomarkers, selected from combinations including measured and/or derived biomarkers. Following this, other measured and/or derived biomarkers were grouped based on their correlation to the best diagnostic biomarkers, with the ability of biomarkers within these groups to act as a diagnostic signature then being assessed.

The results set out in detail below highlight that as long as the above-described criteria are met, the resulting signatures provide the required discriminatory ability for use in diagnosing the presence, absence, degree or prognosis of at least one condition in a biological subject.

Signature Derivation

An illustrative process for the identification of mRNA biomarkers for use in diagnostic algorithms will now be described.

Summary

Peripheral blood samples were obtained from healthy controls and patients retrospectively diagnosed by a panel of physicians with either inSIRS or ipSIRS (blood culture positive). ipSIRS patients were further classified retrospectively into “mild”, “severe” or “shock” based on clinical parameters. Total RNA from patient samples was then used in gene expression analysis (GeneChip® and/or quantitative PCR (qPCR)). Gene expression data were analyzed using a variety of statistical approaches to identify individual and derived markers. Derived markers were divided into groups based on how they correlated to each of the markers in the top-performing (based on AUC) ratio. This ratio approach provides the best diagnostic power with respect to AUC for separating: healthy and post-surgical (PS) (also referred to herein as “inSIRS”) conditions; healthy and sepsis (also referred to herein as “ipSIRS”); inSIRS and ipSIRS; mild ipSIRS and severe ipSIRS; mild ipSIRS and septic shock; and severe ipSIRS and septic shock.

Clinical Trials

Clinical trials were performed to determine whether certain mRNA transcripts could distinguish between healthy controls and various patient groups and within patient groups. Intensive care sepsis, post-surgical and inSIRS patients, as well as healthy controls were prospectively enrolled and attended a single visit where blood was collected for gene expression and mRNA analyses using Affymetrix exon arrays and/or quantitative real-time PCR (qRT-PCR). A definitive diagnosis of infection-positive SIRS (mild, severe or shock) or inSIRS was unlikely to be known at the time patients were enrolled, and thus confirmation of a diagnosis and the assignment of patients to the cohorts were made retrospectively.

Patients who had clinical signs and/or symptoms of ipSIRS or inSIRS were consented and enrolled into the study as soon as possible after they had been identified, in most cases within 24 hours of admission. Final assessment of whether the participant had inSIRS, ipSIRS (mild, severe, or shock) was made retrospectively as clinical information and blood culture results became available.

Study participants were all over 18 years and either they or their surrogate decision maker signed and dated the clinical trial information sheet and consent form. All of the control participants were considered to be in good health based on a brief physical examination and their medical history at the time of enrolment.

Patients or their surrogate decision maker were offered the opportunity to participate in this study if the patient presented with signs and symptoms of either inSIRS or ipSIRS at the time of admission to ICU (using criteria based on the American College of Physicians and the Society of Critical Care Medicine standard definitions). That is, inSIRS and ipSIRS participants needed a variable combination of clinical conditions including two or more of the following within the last 24 hours: temperature >38° C. or <36° C.; heart rate >90 beats/min; respiratory rate >20 breathes/min or a PaCO₂ of <4.3 kPa (<32 mm Hg); and evidence of a white blood cell count <4,000 cells/mm³ (<4×10⁹ cells/L) or >12,000 cells/mm³ (>12×10⁹ cells/L) or >10% immature neutrophils (band forms). Participants were excluded if they had any chronic systemic immune-inflammatory disorders including SLE, Crohn's disease, insulin-dependent diabetes mellitus (IDDM); were transplant recipients or were currently receiving chemotherapy treatment for cancer. Most patients had other underlying co-morbidities. All study participants were 18 years of age or older and had a body mass index of less than 40.

Demography, vital signs measurements (blood pressure, heart rate, respiratory rate, oxygen saturation, temperature), hematology (full blood count), clinical chemistry (urea, electrolytes, liver function enzymes, blood glucose) as well as microbial status was recorded.

Blood was collected for the purpose of extraction of high quality RNA into PAXgene™ tubes (PreAnalytix Inc., Valencia, Calif., USA). Blood for bacterial culture was collected into BacTec Plus Aerobic (10 ml) and BacTec Plus Anaerobic (10 mL) tubes (Becton Dickinson) tubes for the detection of aerobic and anaerobic bacterial growth respectively.

A PAXgene blood RNA kit available from Qiagen Inc. (Valencia, Calif., USA) was used to isolate total RNA from PAXgene tubes. Isolation begins with a centrifugation step to pellet nucleic acids in the PAXgene blood RNA tube. The pellet is washed and re-suspended and incubated in optimized buffers together with Proteinase K to bring about protein digestion. An additional centrifugation is carried out to remove residual cell debris and the supernatant is transferred to a fresh microcentrifuge tube. Ethanol is added to adjust binding conditions, and the lysate is applied to the PAXgene RNA spin column. During brief centrifugation, RNA is selectively bound to the silica-gel membrane as contaminants pass through. Remaining contaminants are removed in three efficient wash steps and RNA is then eluted in Buffer BR5. Determination of RNA quantity and quality was performed using an Agilent Bioanalyzer and Absorbance 260/280 ratio using a spectrophotometer.

Processing of Samples

Measurement of specific mRNA levels in a tissue sample can be achieved using a variety of technologies. A common and readily available technology that covers most of the known human mRNAs is GeneChip® analysis using Affymetrix technology. Details on the technology and methodology can be found at www.affymetrix.com. GeneChip® analysis has the advantage of being able to analyze thousands of RNA transcripts at a time. Another common and readily available technology is qPCR (quantitative polymerase chain reaction), which has the advantage of being able to analyze, in real-time and quantitatively, hundreds of RNA transcripts at a time. Details on one of these technologies (TaqMan®), chemistries and methodologies can be found on the Life Technologies website including published protocols entitled; “Protocol: Introduction to TaqMan SYBR Green Chemistries for Real-Time PCR” and “TaqMan Gene Expression Assays Protocol.” Both GeneChip® and qPCR were used in the discovery and proof-of-concept stages for biomarker identification. qPCR was used exclusively for biomarker feasibility testing.

Analysis, Interpretation of Data and Selection of Biomarkers and Derived Biomarkers

Healthy Control Versus inSIRS

A list of 941 mRNA individual markers with an AUC of at least 0.7 for separating the two conditions of Healthy and inSIRS was generated. FIG. 8A plots these markers against the AUC and FIG. 8B is a box and whisker plot of the best mRNA biomarker for separating the two conditions (AGFG1—ArfGAP with FG repeats 1). The conditions of Healthy and inSIRS are perfectly separated when using this mRNA biomarker alone.

From the 941 individual markers at least 1000 derived markers (ratios) were generated that had an AUC of 1.0. A plot of the AUC of these derived markers for separating the conditions of Healthy and inSIRS is shown in FIG. 8C with the top performing ratio shown as a box and whisker plot in FIG. 8D. The AUC for AGFG1 and PVRIG (poliovirus receptor related immunoglobulin domain containing) has a ratio is 1.0.

All 941 individual markers were then broken into two groups—those that correlate to AGFG1 and those that correlate to PVRIG. FIGS. 8E and 8F demonstrate the correlation between the two groups based on their similarity to either AGFG1 or PVRIG. In these plots the groups are referred to as either group 1 (AGFG1) or group 2 (PVRIG). It can be seen that each “group” contains those markers that are most highly correlated to each other.

The markers in each “group” also correlate strongly (as demonstrated by AUC greater than 0.7 for all markers) to the condition being studied (in this instance Healthy versus inSIRS (PS)) as shown in FIGS. 8G and 8H.

By choosing mRNAs from these two groups to create derived markers a better AUC for separating Healthy and inSIRS is obtained than when markers are chosen from within groups (p<2.558e-13) as demonstrated in FIG. 8I, which shows a greater overall AUC is obtained compared to when using markers from either group 1 or group 2 alone. The mean AUC for markers derived from groups 1 and 2 is over 0.97, whereas the mean AUC for markers derived from either group 1 or 2 alone is less than 0.9.

Healthy Control Versus ipSIRS

A list of 941 mRNA individual markers with an AUC of at least 0.7 for separating the two conditions of Healthy and ipSIRS was generated. FIG. 9A plots these markers against the AUC and FIG. 9B is a box and whisker plot of the best mRNA biomarker for separating the two conditions (LTBP3—latent transforming growth factor beta binding protein 3). The conditions of Healthy and ipSIRS are perfectly separated when using this mRNA biomarker alone.

From the 941 individual markers at least 1000 derived markers (ratios) were generated that had an AUC of 1.0. A plot of the AUC of these derived markers for separating the conditions of Healthy and inSIRS is shown in FIG. 9C with the top performing ratio shown as a box and whisker plot in FIG. 9D. The AUC for LTBP3 and LPHN1 (latrophilin 1) as a ratio is 1.0.

All 941 individual markers were then broken into two groups—those that correlate to LTBP3 and those that correlate to LPHN1. Both plots in FIGS. 9E and 9F demonstrate the correlation between the two groups based on their similarity to either LTBP3 or LPHN1. It can be seen that each “group” contains those markers that are most highly correlated to each other.

The markers in each “group” also correlate strongly (as demonstrated by AUC greater than 0.7 for all markers) to the condition being studied (in this instance Healthy versus ipSIRS (sepsis)), as shown in FIGS. 9G and 9H.

By choosing mRNAs from these two groups to create derived markers a better AUC for separating Healthy and ipSIRS is obtained than when markers are chosen from within groups (p<2.2e-16) as demonstrated in FIG. 9I, which shows an improved AUC compared to using markers from either group 1 or group 2 alone. The mean AUC for markers derived from groups 1 and 2 is over 0.97, whereas the mean AUC for markers derived from either group 1 or 2 alone is less than 0.8.

inSIRS Versus ipSIRS

A list of 359 mRNA individual markers with an AUC of at least 0.7 for separating the two conditions of inSIRS and ipSIRS was generated. FIG. 10A plots these markers against the AUC and FIG. 10B is a box and whisker plot of the best mRNA biomarker for separating the two conditions (PIWIL4—piwi-like RNA mediated gene silencing 4). The conditions of inSIRS (PS) and ipSIRS (sepsis) are well separated when using this mRNA biomarker alone.

From the 359 individual markers 1000 derived markers (ratios) were generated that had an AUC greater than 0.9. A plot of the AUC of these derived markers for separating the conditions of inSIRS and ipSIRS is shown in FIG. 10C with the top performing ratio of PLA2G7 (phospholipase A2, Group VII, (platelet activating factor acetyl hydrolase, plasma)) and PLAC8 (placenta-specific 8) shown as a box and whisker plot in FIG. 10D.

All 359 individual markers were then broken into two groups—those that correlate to PLA2G7 and those that correlate to PLAC8. The plot in FIG. 10E demonstrates that the markers in each “group” correlate strongly (as demonstrated by AUC greater than 0.7 for all markers) to the condition being studied (in this instance inSIRS (PS) versus ipSIRS (sepsis)).

By choosing mRNAs from these two groups to create derived markers a better AUC for separating inSIRS (PS) and ipSIRS (sepsis) is obtained than when markers are chosen from within groups (p<5.78e-5) as demonstrated in FIG. 10F compared to when using markers from either group 1 or group 2 alone. The mean AUC for markers derived from groups 1 and 2 is over 0.80, whereas the mean AUC for markers derived from either group 1 or 2 alone is less than 0.8.

In an alternative embodiment, the markers were broken into four groups—those that correlate to CEACAM4 (bucket 3), those that correlate to LAMP1 (bucket 4), those that correlate to PLA2G7 (bucket 1) and those that correlate to PLAC8 (bucket 2). The plots in FIG. 10G demonstrate the markers in each group correlate strongly (as demonstrated by AUC greater than 0.7 for all markers) to the condition being studied (i.e., inSIRS (PS) versus ipSIRS (sepsis)). Like the two-bucket embodiment discussed above, choosing mRNAs from four groups to create derived markers results in an overall better AUC for separating inSIRS (PS) and ipSIRS (sepsis), as compared to when markers are chosen from within groups (p<0.2564, as demonstrated in FIG. 10H compared to when using markers from any one of groups 1 to 4. The mean AUC for markers derived from groups 1 to 4 is over 0.8 whereas the mean AUC for markers derived from any one of groups 1 to 4 is less than 0.8

Mild ipSIRS Versus Severe ipSIRS

A list of 66 mRNA individual markers with an AUC of at least 0.7 for separating the two conditions of mild ipSIRS and severe ipSIRS was generated. FIG. 11A plots these markers against the AUC and FIG. 11B is a box and whisker plot of the best mRNA biomarker for separating the two conditions (N4BP2L2—NEDD4 binding protein 2-like 2). The conditions of mild ipSIRS and severe ipSIRS are well separated when using this mRNA biomarker alone.

From the 66 individual markers at least 1000 derived markers (ratios) were generated that had an AUC of 0.87. A plot of the AUC of these derived markers for separating the conditions of mild ipSIRS and severe ipSIRS is shown in FIG. 11C with the top performing ratio shown as a box and whisker plot in FIG. 11D. The AUC for N4BP2L2 and ZC3H11A (zinc finger CCCH-type containing 11A) as a ratio is 0.983.

All 66 individual markers were then broken into two groups—those that correlate to N4BP2L2 and those that correlate to ZC3H11A. Both plots in FIGS. 11E and 11F demonstrate the correlation between the two groups based on their similarity to either N4BP2L2 or ZC3H11A. In these plots the groups are referred to as either group 1 (N4BP2L2) or group 2 (ZC3H11A). It can be seen that each “group” contains those markers that are most highly correlated to each other. The markers in each “group” also correlate strongly (as demonstrated by AUC greater than 0.7 for all markers) to the condition being studied (in this instance mild ipSIRS versus severe ipSIRS).

By choosing mRNAs from these two groups to create derived markers a better AUC for separating mild ipSIRS and severe ipSIRS is obtained than when markers are chosen from within groups (p<2.2e-16) as demonstrated in FIG. 11I, compared to when using markers from either group 1 or group 2 alone. The mean AUC for markers derived from groups 1 and 2 is over 0.89, whereas the mean AUC for markers derived from either group 1 or 2 alone is less than 0.6.

Mild ipSIRS Versus ipSIRS—Shock

A list of 48 mRNA individual markers with an AUC of at least 0.7 for separating the two conditions of mild ipSIRS and ipSIRS—shock was generated. FIG. 12A plots these markers against the AUC and FIG. 12B is a box and whisker plot of the best mRNA biomarker for separating the two conditions (CD6-CD6 molecule). The conditions of mild ipSIRS and ipSIRS shock are well separated when using this mRNA biomarker alone.

From the 48 individual markers at least 1000 derived markers (ratios) were generated that had an AUC of at least 0.793. A plot of the AUC of these derived markers for separating the conditions of mild ipSIRS and ipSIRS shock is shown in FIG. 12C with the top performing ratio shown as a box and whisker plot in FIG. 12D. The AUC for VAMP2 and UBAP1 (ubiquitin associated protein 1) as a ratio is 0.978.

All 48 individual markers were then broken into two groups—those that correlate to VAMP2 and those that correlate to UBAP1. Both plots in FIGS. 12E and 12F demonstrate the correlation between the two groups based on their similarity to either VAMP2 or UBAP1. In these plots the groups are referred to as either group 1 (VAMP2) or group 2 (UBAP1). It can be seen that each “group” contains those markers that are most highly correlated to each other. The markers in each “group” also correlate strongly (as demonstrated by AUC greater than 0.7 for all markers) to the condition being studied (in this instance mild ipSIRS versus ipSIRS—shock).

By choosing mRNAs from these two groups to create derived markers a better AUC for separating mild ipSIRS and ipSIRS—shock is obtained than when markers are chosen from within groups (p<2.2e-16) as demonstrated in FIG. 12I, compared to when using markers from either group 1 or group 2 alone. The mean AUC for markers derived from groups 1 and 2 is over 0.87, whereas the mean AUC for markers derived from either group 1 or 2 alone is less than 0.65.

Severe ipSIRS Versus ipSIRS—Shock

A list of 61 mRNA individual markers with an AUC of at least 0.7 for separating the two conditions of severe ipSIRS and ipSIRS—shock was generated. FIG. 13A plots these markers against the AUC and FIG. 13B is a box and whisker plot of the best mRNA biomarker for separating the two conditions (SIRPG—signal regulatory protein gamma) The conditions of severe ipSIRS and ipSIRS—shock are well separated when using this mRNA biomarker alone.

From the 61 individual markers at least 1000 derived markers (ratios) were generated that had an AUC of at least 0.821. A plot of the AUC of these derived markers for separating the conditions of severe ipSIRS and ipSIRS—shock is shown in FIG. 13C with the top performing ratio shown as a box and whisker plot in FIG. 13D. The AUC for GATA3 (GATA binding protein 3) and MECOM (MDS1 and EVI1 complex locus) as a ratio is 0.936.

All 61 individual markers were then broken into two groups—those that correlate to GATA3 and those that correlate to MECOM. Both plots in FIGS. 13E and 13F demonstrate the correlation between the two groups based on their similarity to either GATA3 or MECOM. In these plots the groups are referred to as either group 1 (GATA3) or group 2 (MECOM). It can be seen that each “group” contains those markers that are most highly correlated to each other. The markers in each “group” also correlate strongly (as demonstrated by AUC greater than 0.7 for all markers) to the condition being studied (in this instance severe ipSIRS versus ipSIRS—shock).

By choosing mRNAs from these two groups to create derived markers a better AUC for separating severe ipSIRS and ipSIRS—shock is obtained than when markers are chosen from within groups (p<2.2e-16) as demonstrated in FIG. 13I compared to when using markers from either group 1 or group 2 alone. The mean AUC for markers derived from groups 1 and 2 is over 0.82, whereas the mean AUC for markers derived from either group 1 or 2 alone is less than 0.7.

Signature Usage

Use of the above described markers and resulting signatures in patient populations and benefits in respect of differentiating inSIRS and ipSIRS, will now be described.

An assay capable of differentiating patients with inSIRS and ipSIRS can be used in multiple patient populations including:

1) Intensive Care Unit (medical and surgical ICU)

2) Post-surgical and medical wards

3) Emergency Department

4) Medical clinics.

Patients admitted to intensive care (ICU) often have ipSIRS, or develop ipSIRS during their ICU stay. The ultimate aim of intensive care is to ensure the patient survives and is discharged to a general ward in the minimum time. Patients in intensive care with diagnosed ipSIRS are usually administered a number of therapeutic compounds—many of which have opposing actions on the immune system and many of which could be counterproductive depending on the severity of ipSIRS (mild sepsis, severe sepsis, septic shock). Monitoring intensive care patients on a regular basis with biomarkers of the present invention will allow medical practitioners to differentiate inSIRS from ipSIRS and determine the stage of ipSIRS and hence choice of therapies, when to start and stop therapies, and patient management procedures, and ultimately response to therapy. Information provided by these biomarkers will therefore allow medical intensivists to tailor and modify therapies to ensure patients survive and spend less time in intensive care. Less time in intensive care leads to considerable savings in medical expenses including through less occupancy time and appropriate use and timing of medications.

Surgical and general medical patients often develop inSIRS post-surgery or as a consequence of their condition or procedures and have a higher risk of developing ipSIRS. Post-operative and medical care in such patients therefore involves monitoring for signs of inSIRS and ipSIRS and differentiating between these two conditions. The treatment and management of inSIRS and ipSIRS patients post-surgically and in general wards is different, since inSIRS patients can be put on mild anti-inflammatory drugs or anti-pyretics and ipSIRS patients must be started on antibiotics as soon as possible for best outcomes. Monitoring post-surgical and medical patients on a regular basis with biomarkers of the present invention will allow nursing and medical practitioners to differentiate inSIRS and ipSIRS at an early stage and hence make informed decisions on choice of therapies and patient management procedures, and ultimately response to therapy. Information provided by these biomarkers will therefore allow medical practitioners to tailor and modify therapies to ensure patients recover quickly from surgery or other condition and do not develop ipSIRS. Less time in hospital and less complications leads to considerable savings in medical expenses including through less occupancy time and appropriate use and timing of medications.

Further, patients presenting to emergency departments often have a fever, which is one (of four) of the clinical signs of inSIRS. Such patients need to be assessed to determine if they have either inSIRS or ipSIRS. As mentioned above, the treatment and management of pyretic, inSIRS and septic patients are different. By way of example, a patient with a fever without other inSIRS clinical signs and no obvious source of infection may be sent home, or provided with other non-hospital services, without further hospital treatment. However, a patient with a fever may have early ipSIRS and not admitting such a patient may put their life at risk. Because these biomarkers can differentiate inSIRS and ipSIRS they will allow medical practitioners to triage emergency department patients quickly and effectively. Accurate triage decision-making insures that patients requiring hospital treatment are given it, and those that don't are provided with other appropriate services.

Further still, patients presenting to medical clinics often have any one of the four clinical signs of inSIRS (increased heart rate, increased respiratory rate, abnormal white blood cell count, fever or hypothermia). Many different clinical conditions can present with one of the four clinical signs of inSIRS and such patients need to be assessed to determine if they have either inSIRS or ipSIRS and to exclude other differential diagnoses. By way of example, a patient with colic might also present with clinical signs of increased heart rate. Differential diagnoses could be (but not limited to) appendicitis, urolithiasis, cholecystitis, pancreatitis, enterocolitis. In each of these conditions it would be important to determine if there was a systemic inflammatory response (inSIRS) or whether an infection was contributing to the systemic response to the condition (ipSIRS). The treatment and management of patients with and without systemic inflammation and/or infection are different. Because these biomarkers can differentiate patients with a systemic inflammatory response to infection from those with a systemic inflammatory response without infection (inSIRS and ipSIRS), and determine the degree of systemic involvement, the use of them will allow medical practitioners to determine the next medical procedure(s) to perform to satisfactorily resolve the patient issue.

Determining the Extent of Systemic Inflammation in Sick Patients and Those with inSIRS and ipSIRS

As mentioned above, patients presenting to medical clinics often have any one of the four clinical signs of inSIRS. However, many different clinical conditions can present with one of the four clinical signs of inSIRS and such patients need to be assessed to determine if they have inSIRS, and if so the extent of inSIRS, or ipSIRS, and if so the extent of ipSIRS, and to exclude other differential diagnoses.

By way of example, a patient with respiratory distress is likely to present with clinical signs of increased respiratory rate. Differential diagnoses could be (but not limited to) asthma, pneumonia, congestive heart failure, physical blockage of airways, allergic reaction, collapsed lung, pneumothorax. In each of these conditions it would be important to determine if there was a systemic inflammatory response (inSIRS) or whether an infection was contributing to the condition. The treatment and management of patients with and without systemic inflammation and/or infection are different. Because these biomarkers can differentiate patients with a systemic inflammatory response to infection from those with a systemic inflammatory response without infection (inSIRS and ipSIRS), and determine the degree of systemic involvement, the use of them will allow medical practitioners to determine the next medical procedure(s) to perform to satisfactorily resolve the patient issue. Patients with a collapsed lung, pneumothorax or a physical blockage are unlikely to have a large systemic inflammatory response and patients with congestive heart failure, allergic reaction or asthma are unlikely to have a large systemic inflammatory response due to infection. The extent of both inSIRS and ipSIRS, as indicated by biomarkers presented in this patent, also provides clinicians with information on next treatment and management steps. For example, a patient with respiratory distress and a strong biomarker response indicating ipSIRS is likely to be immediately hospitalized, placed on antibiotics and a chest X-ray performed. A patient with respiratory distress and a strong biomarker response indicating inSIRS but not ipSIRS is likely to be hospitalized and chest X-rayed along with other investigative diagnostic procedures, such as MRI, ECG, and angiogram. A patient with respiratory distress with a short history and no inSIRS or ipSIRS is likely to undergo further examination at a local clinic rather than requiring hospitalization.

Again, and as mentioned above, patients presenting to emergency departments often have a fever, which is one (of four) of the clinical signs of inSIRS. Such patients need to be assessed to determine if they have either inSIRS or ipSIRS. Further it is important to determine how sick they are to be able to make a Judgment call on whether to admit the patient or not. Accurate triage decision-making insures that patients requiring hospital treatment are given it, and those that don't are provided with other appropriate services.

Patients in ICU often have inSIRS and ipSIRS and it is important to differentiate these two conditions as treatment regimens differ. In patients with inSIRS it is important to determine the extent of the inflammatory response so that appropriate treatments and management regimens can be put in place. For example, a patient newly determined to have inSIRS that is not extensive may be able to be put on mild medication such a non-steroidal anti-inflammatory. A patient newly determined to have extensive inSIRS (e.g. trauma) may require stronger anti-inflammatory medication such as steroids to reduce the potential impact of the side effects of inflammation (swelling). In patients with ipSIRS it is also important to determine the extent of the inflammatory response to infection so that appropriate treatments and management regimens can be put in place or stopped. For example, for a patient with a persistent strong ipSIRS response the clinician may consider either changing, or adding to, the antibiotic treatment regimen in the absence of traditional bacterial culture and sensitivity results. Further, patients that are known to have had ipSIRS and have been on antimicrobial therapy for an extended period but have since demonstrated (by testing using biomarkers) that they no longer have either an inSIRS or ipSIRS can therefore be safely taken off intravenous antibiotics.

Determining the Severity of ipSIRS

Patients admitted to intensive care (ICU) often have ipSIRS, or develop ipSIRS during their ICU stay. It is known that the severity of sepsis can be considered to be a continuum from less severe, or sepsis, to more severe, or severe sepsis, to the most severe, or septic shock. More severe sepsis (ipSIRS) requires more aggressive, immediate and tailored intervention compared to sepsis (although all are acute conditions). Patients in intensive care with diagnosed ipSIRS are usually administered a number of therapeutic compounds—many of which have opposing actions on the immune system and many of which could be counterproductive depending on the severity of ipSIRS (sepsis, severe sepsis, septic shock). Monitoring intensive care patients on a regular basis with biomarkers of the present invention will allow medical practitioners to determine the severity of ipSIRS (mild, severe or shock) and hence choice of therapies and patient management procedures, and ultimately response to therapy. Information provided by these biomarkers disclosed herein will therefore allow medical practitioners to tailor, modify or cease therapies and/or care to ensure patients survive and spend less time in intensive care. Less time in intensive care leads to considerable savings in medical expenses including through less occupancy time and appropriate use and timing of medications.

First Example Workflow

A first example workflow will now be described. The workflow involves up to seven steps depending upon availability of automated platforms. The assay uses quantitative, real-time determination of the amount of each transcript in the sample based on the detection of fluorescence on a qRT-PCR instrument (e.g. Applied Biosystems 7500 Fast Dx Real-Time PCR Instrument, Applied Biosystems, Foster City, Calif., catalogue number 440685; K082562). Transcripts are each amplified, detected, and quantified in a separate reaction well using a probe that is visualized in the FAM channel (by example). The reported score is calculated using interpretive software provided separately to the kit but designed to integrate with RT-PCR machines.

The workflow below describes the use of manual processing and a pre-prepared kit.

Pre-Analytical

Blood collection

Total RNA isolation

Analytical

Reverse transcription (generation of cDNA)

qPCR preparation

qPCR

Software, Interpretation of Results and Quality Control

Output.

Kit Contents

Diluent

RT Buffer

RT Enzyme Mix

qPCR Buffer

Primer/Probe Mix

AmpliTaq Gold® (or similar)

High Positive Control

Low Positive Control

Negative Control

Blood Collection

The specimen used is a 2.5 mL sample of blood collected by venipuncture using the PAXgene® collection tubes within the PAXgene® Blood RNA System (Qiagen, kit catalogue #762164; Becton Dickinson, Collection Tubes catalogue number 762165; K042613). An alternate collection tube is Tempus® (Life Technologies).

Total RNA Isolation

Blood (2.5 mL) collected into a PAXgene RNA tube is processed according to the manufacturer's instructions. Briefly, 2.5 mL sample of blood collected by venipuncture using the PAXgene™ collection tubes within the PAXgene™ Blood RNA System (Qiagen, kit catalogue #762164; Becton Dickinson, Collection Tubes catalogue number 762165; K042613). Total RNA isolation is performed using the procedures specified in the PAXgene™ Blood RNA kit (a component of the PAXgene™ Blood RNA System). The extracted RNA is then tested for purity and yield (for example by running an A_(260/280) ratio using a Nanodrop® (Thermo Scientific)) for which a minimum quality must be (ratio >1.6). RNA should be adjusted in concentration to allow for a constant input volume to the reverse transcription reaction (below). RNA should be processed immediately or stored in single-use volumes at or below −70° C. for later processing.

Reverse Transcription

Determine the appropriate number of reaction equivalents to be prepared (master mix formulation) based on a plate map and the information provided directly below. Each clinical specimen is run in singleton.

-   -   a) Each batch run must include the following specimens:     -   b) High Control, Low Control, Negative Control, and No Template         Control (Test Diluent instead of sample) in singleton each         Program the ABI 7500 Fast Dx Instrument as detailed below.     -   c) Launch the software.     -   d) Click Create New Document     -   e) In the New Document Wizard, select the following options:         -   i) Assay: Standard Curve (Absolute Quantitation)         -   ii) Container: 96-Well Clear         -   iii) Template: Blank Document (or select a             laboratory-defined template)         -   iv) Run Mode: Standard 7500         -   v) Operator: Enter operator's initials         -   vi) Plate name: [default]     -   f) Click Finish     -   g) Select the Instrument tab in the upper left     -   h) In the Thermal Cycler Protocol area, Thermal Profile tab,         enter the following times:         -   i) 25° C. for 10 minutes         -   ii) 45° C. for 45 minutes         -   iii) 93° C. for 10 minutes         -   iv) Hold at 25° C. for 60 minutes

In a template-free area, remove the test Diluent and RT-qPCR Test RT Buffer to room temperature to thaw. Leave the RT-qPCR Test RT Enzyme mix in the freezer and/or on a cold block.

In a template-free area, assemble the master mix in the order listed below.

RT Master Mix—Calculation:

Per well ×N RT-qPCR Test RT Buffer 3.5 μL 3.5 × N RT-qPCR Test RT Enzyme mix 1.5 μL 1.5 × N Total Volume   5 μL   5 × N

Gently vortex the master mix then pulse spin. Add the appropriate volume (5 μL) of the RT Master Mix into each well at room temperature.

Remove clinical specimens and control RNAs to thaw. (If the specimens routinely take longer to thaw, this step may be moved upstream in the validated method.)

Vortex the clinical specimens and control RNAs, then pulse spin. Add 10 μL of control RNA or RT-qPCR Test Diluent to each respective control or negative well.

Add 10 μL of sample RNA to each respective sample well (150 ng total input for RT; OD₂₆₀/OD₂₈₀ ratio greater than 1.6). Add 10 μL of RT-qPCR Test Diluent to the respective NTC well.

Note: The final reaction volume per well is 15 μL.

Samples RT Master Mix  5 μL RNA sample 10 μL Total Volume (per well) 15 μL

Mix by gentle pipetting. Avoid forming bubbles in the wells.

Cover wells with a seal.

Spin the plate to remove any bubbles (1 minute at 400× g).

Rapidly transfer to ABI 7500 Fast Dx Instrument pre-programmed as detailed above.

Click Start. Click Save and Continue. Before leaving the instrument, it is recommended to verify that the run started successfully by displaying a time under Estimated Time Remaining.

qPCR master mix may be prepared to coincide roughly with the end of the RT reaction. For example, start about 15 minutes before this time. See below.

When RT is complete (i.e. resting at 25° C.; stop the hold at any time before 60 minutes is complete), spin the plate to collect condensation (1 minute at 400× g).

qPCR Preparation

Determine the appropriate number of reaction equivalents to be prepared (master mix formulation) based on a plate map and the information provided in RT Preparation above.

Program the ABI 7500 Fast Dx with the settings below.

-   -   a) Launch the software.     -   b) Click Create New Document     -   c) In the New Document Wizard, select the following options:         -   i) Assay: Standard Curve (Absolute Quantitation)         -   ii) Container: 96-Well Clear         -   iii) Template: Blank Document (or select a             laboratory-defined template)         -   iv) Run Mode: Standard 7500         -   v) Operator: Enter operator's initials         -   vi) Plate name: Enter desired file name     -   d) Click Next     -   e) In the Select Detectors dialog box:         -   i) Select the detector for the first biomarker, and then             click Add>>.         -   ii) Select the detector second biomarker, and then click             Add>>, etc.         -   iii) Passive Reference: ROX     -   f) Click Next     -   g) Assign detectors to appropriate wells according to plate map.         -   i) Highlight wells in which the first biomarker assay will             be assigned         -   ii) Click use for the first biomarker detector         -   iii) Repeat the previous two steps for the other biomarkers         -   iv) Click Finish     -   h) Ensure that the Setup and Plate tabs are selected     -   i) Select the Instrument tab in the upper left     -   j) In the Thermal Cycler Protocol area, Thermal Profile tab,         perform the following actions, with the results shown in FIG.         14:         -   i) Delete Stage 1 (unless this was completed in a             laboratory-defined template).         -   ii) Enter sample volume of 25 μL.         -   iii) 95° C. 10 minutes         -   iv) 40 cycles of 95° C. for 15 seconds, 63° C. for 1 minute         -   v) Run Mode: Standard 7500         -   vi) Collect data using the “stage 2, step 2 (63.0@1:00)”             setting     -   k) Label the wells as below using this process: Right click over         the plate map, then select Well Inspector. With the Well         Inspector open, select a well or wells. Click back into the Well         Inspector and enter the Sample Name. Close the Well Inspector         when completed.         -   i) CONH for High Control         -   ii) CONL for Low Control         -   iii) CONN for Negative Control         -   iv) NTC for No Template Control         -   v) [Accession ID] for clinical specimens     -   l) Ensure that detectors and quenchers are selected as listed         below.         -   i) FAM for CEACAM biomarker 1; quencher=none         -   ii) FAM for LAMP1 biomarker 2; quencher=none, etc.         -   iii) FAM for PLA2G7; quencher=none         -   iv) FAM for PLACE; quencher=none         -   v) Select “ROX” for passive reference             qPCR

In a template-free area, remove the assay qPCR Buffer and assay Primer/Probe Mixes for each target to room temperature to thaw. Leave the assay AmpliTaq Gold in the freezer and/or on a cold block.

Still in a template-free area, prepare qPCR Master Mixes for each target in the listed order at room temperature.

qPCR Master Mixes—Calculation Per Sample

Per well ×N qPCR Buffer  11 μL  11 × N Primer/Probe Mix 3.4 μL 3.4 × N AmpliTaq Gold ® 0.6 μL 0.6 × N Total Volume  15 μL  15 × N

Gently mix the master mixes by flicking or by vortexing, and then pulse spin. Add 15 μL of qPCR Master Mix to each well at room temperature.

In a template area, add 130 μL of SeptiCyte Lab Test Diluent to each cDNA product from the RT Reaction. Reseal the plate tightly and vortex the plate to mix thoroughly.

Add 10 μL of diluted cDNA product to each well according to the plate layout.

Mix by gentle pipetting. Avoid forming bubbles in the wells.

Cover wells with an optical seal.

Spin the plate to remove any bubbles (1 minute at 400× g).

Place on real-time thermal cycler pre-programmed with the settings above.

Click Start. Click Save and Continue. Before leaving the instrument, it is recommended to verify that the run started successfully by displaying a time under Estimated Time Remaining.

Note: Do not open the qPCR plate at any point after amplification has begun. When amplification has completed, discard the unopened plate.

Software, Interpretation of Results and Quality Control

Software is specifically designed to integrate with the output of PCR machines and to apply an algorithm based on the use of multiple biomarkers. The software takes into account appropriate controls and reports results in a desired format.

When the run has completed on the ABI 7500 Fast Dx Instrument, complete the steps below in the application 7500 Fast System with 21 CFR Part 11 Software, ABI software SDS v1.4.

Click on the Results tab in the upper left corner.

Click on the Amplification Plot tab in the upper left corner.

In the Analysis Settings area, select an auto baseline and manual threshold for all targets. Enter 0.01 as the threshold.

Click on the Analyze button on the right in the Analysis Settings area.

From the menu bar in the upper left, select File then Close.

Complete the form in the dialog box that requests a reason for the change. Click OK.

Transfer the data file (.sds) to a separate computer running the specific assay RT-qPCR Test Software.

Launch the assay RT-qPCR Test Software. Log in.

From the menu bar in the upper left, select File then Open.

Browse to the location of the transferred data file (.sds). Click OK.

The data file will then be analyzed using the assay's software application for interpretation of results.

Interpretation of Results and Quality Control

Results

Launch the interpretation software. Software application instructions are provided separately.

Following upload of the .sds file, the Software will automatically generate classifier scores for controls and clinical specimens.

Controls

The Software compares each CON (control) specimen (CONH, CONL, CONN) to its expected result. The controls are run in singleton.

Control specimen Designation Name Expected result CONH High Control Score range CONL Low Control Score range CONN Negative Control Score range NTC No Template Fail (no Ct for all Control targets)

If CONH, CONL, and/or CONN fail the batch run is invalid and no data will be reported for the clinical specimens. This determination is made automatically by the interpretive software. The batch run should be repeated starting with either a new RNA preparation or starting at the RT reaction step.

If NTC yields a result other than Fail (no Ct for all targets), the batch run is invalid and no data may be reported for the clinical specimens. This determination is made by visual inspection of the run data. The batch run should be repeated starting with either a new RNA preparation or starting at the RT reaction step.

If a second batch run fails, please contact technical services. If both the calibrations and all controls are valid, then the batch run is valid and specimen results will be reported.

Specimens

Note that a valid batch run may contain both valid and invalid specimen results.

Analytical criteria (e.g. Ct values) that qualify each specimen as passing or failing (using pre-determined data) are called automatically by the software.

Scores out of range—reported.

Quality Control

Singletons each of the Negative Control, Low Positive Control, and High Positive Control must be included in each batch run. The batch is valid if no flags appear for any of these controls.

A singleton of the No Template Control is included in each batch run and Fail (no Ct for all targets) is a valid result indicating no amplifiable material was detectable in the well.

The negative control must yield a Negative result. If the negative control is flagged as Invalid, then the entire batch run is invalid.

The low positive and high positive controls must fall within the assigned ranges. If one or both of the positive controls are flagged as Invalid, then the entire batch run is invalid.

Example Output

A possible example output from the software is presented below in FIG. 15. The format of such a report depends on many factors including; quality control, regulatory authorities, cut-off values, the algorithm used, laboratory and clinician requirements, likelihood of misinterpretation.

In this instance the assay is called “SeptiCyte Lab Test”. The result is reported as a number (5.8), a call (“Sepsis Positive”), a position on a 0-12 scale, and a probability of the patient having sepsis based on historical results and the use of a pre-determined cut-off (using results from clinical trials). Results of controls within the assay are also reported. Other information that could be reported might include: previous results and date and time of such results, probability of severe sepsis or septic shock, a scale that provides cut-off values for historical testing results that separate the conditions of healthy, inSIRS and ipSIRS (mild, severe and shock) such that those patients with higher scores are considered to have more severe inSIRS or ipSIRS.

Second Example Workflow

A second example workflow will now be described. Machines have been, and are being, developed that are capable of processing a patient sample at point-of-care, or near point-of-care. Such machines require few molecular biology skills to run and are aimed at non-technical users. The idea is that the sample would be pipetted directly into a disposable cartridge that is then inserted into the machine. The user presses “Start” and within 2-3 hours a result is generated. The cartridge contains all of the required reagents to perform Steps 2-5 in the example workflow above and the machine has appropriate software incorporated to allow Steps 6 and 7 to be performed.

Fresh, whole, anti-coagulated blood can be pipetted into an Idylla Cartridge (Biocartis NV) or similar (Unyvero, Curetis AG; Enigma ML, Enigma Diagnostics; DiagCore, STAT Diagnostica; Savannah, Quidel Corp; ePlex, GenMark Dx), and on-screen instructions on the Idylla machine followed to test for differentiating inSIRS and ipSIRS (by example). Inside the Idylla machine RNA is first extracted from the whole blood and is then converted into cDNA. The cDNA is then used in qRT-PCR reactions. The reactions are followed in real time and Ct values calculated. On-board software generates a result output (see Figure XX). Appropriate quality control measures for RNA quality, no template controls, high and low template controls and expected Ct ranges ensure that results are not reported erroneously.

Example Biomarker Ratios

Example biomarker ratios (the top 12 based on AUC) that are capable of separating different conditions are shown in the box and whisker plots as listed below, with each showing perfect separation.

-   -   FIGS. 16A to 16L show Healthy Versus inSIRS (Post-Surgical)     -   FIGS. 17A to 17L show Healthy Versus ipSIRS (Sepsis)     -   FIGS. 18A to 18L show inSIRS (Post-Surgical) Versus ipSIRS         (Sepsis)     -   FIGS. 19A to 19L show Sepsis Versus Severe Sepsis     -   FIGS. 20A to 20L show Severe Sepsis Versus Septic Shock     -   FIGS. 21A to 21L show Sepsis Versus Septic Shock

Example Algorithm Combining Biomarker Ratios

Biomarker ratios (derived markers) can be used in combination to increase the diagnostic power for separating various conditions. Determining which markers to use, and how many, for separating various conditions can be achieved by calculating Area Under Curve (AUC).

FIG. 22 shows the effect on AUC (in this instance for separating inSIRS and ipSIRS) of adding biomarkers to the diagnostic signature. Diagnostic power significantly increases (adjusted p-value=0.0175) between a single mRNA biomarker (in this instance PLA2G7, AUC of 0.88, 95% CI 0.79-0.97) compared to the power of the two best performing markers in combination (in this instance PLA2G7 and PLAC8, AUC of 0.96, 95% CI 0.91-1.0). Combinations of two, three, four and five biomarkers produced equally as good differentiation of inSIRS and ipSIRS without significant differences. For commercial development of derived markers other factors come into play such as cost-effectiveness, assay complexity and capabilities of the qRT-PCR platform.

In this example, the addition of markers beyond 3 or 4 does not significantly improve performance and, conversely, a decline in AUC is observed in signatures of >5 genes probably because when a statistical model is forced to include biomarkers that add little additional information data over-fitting and addition of noise occurs.

As such, and by example, a 4-gene signature (0.986, 95% CI 0.964-1.00) offers the appropriate balance between simplicity, practicality and commercial risk for separating inSIRS and ipSIRS. Further, an equation using four markers weighs each biomarker equally which also provides additional robustness in cases of analytical or clinical variability.

One example equation that provides good diagnostic power for separating inSIRS and ipSIRS (amongst others) is: Diagnostic Score=(PLA2G7−PLAC8)+(CEACAM4−LAMP1)

The value for each biomarker is a Ct value from a PCR. When clinical samples from patients with inSIRS and ipSIRS were tested using these four markers in a PCR the Ct values for each of the markers was found to fall between 26 and 34. In this patient population the first biomarker within each bracket pair has a higher value than the second biomarker within each bracket pair. Thus, the “Diagnostic Score” has been found to have values between 0 and 12. However, in theory the “Diagnostic Score could potentially be highest Ct value +/− highest Ct value.

In FIG. 23 shows results of PCR and the use of the above algorithm have been calculated for two patient populations (N=63 for “Discovery” and N=70 for “Feasibility”). Each patient was clinically and retrospectively (note, not at the time the sample was taken) confirmed as having either inSIRS (black dots) or ipSIRS (red dots). Each patient sample has also had a SeptiCyte score calculated (Y axis on left hand side). On a scale of 0-12 it can be seen that patients with confirmed ipSIRS (red dots) obtain a higher Diagnostic Score compared to those with confirmed inSIRS. Further, it can be seen that an arbitrary cut-off line can be drawn that more or less separates the two conditions depending upon the desired false negative or false positive rate (compared to a retrospective diagnosis of inSIRS or ipSIRS using clinical data). In this instance the line is drawn at a “SeptiCyte Score” of 4 such that the number of false negative ipSIRS calls in the Discovery Dataset is zero and the number of false negative ipSIRS calls in the Feasibility Dataset is 2. Conversely the number of false positive ipSIRS calls in the Discovery Dataset is four and the number of false positive ipSIRS calls in the Feasibility Dataset is 9. Clearly in this instance whether a patient sample is false positive or false negative depends on the artificial gold standard of a retrospective clinical call of inSIRS or ipSIRS.

Accordingly, in one example, when used for determining a likelihood of the subject having inSIRS or ipSIRS, the method can include determining a first pair of biomarker values indicative of a concentration of polynucleotide expression products of the PLA2G7 gene and PLAC8 gene, determining a second pair of biomarker values indicative of a concentration of polynucleotide expression products of the CEACAM4 gene and LAMP1 gene and then determining an indicator using the first and second pairs of biomarker values.

As previously discussed, the indicator could then be compared to indicator references specifically established to distinguish between inSIRS and ipSIRS.

An example process of a process for establishing indicator references will now be described in more details with reference to FIG. 24.

In this example, at step 2400 the processing system 201 determines reference data in the form of measured biomarker values obtained for a reference population. The reference data may be acquired in any appropriate manner but typically this involves obtaining gene expression product data from a plurality of individuals.

In order to achieve this, gene expression product data are collected, for example by obtaining a biological sample, such as a peripheral blood sample, and then performing a quantification process, such as a nucleic acid amplification process, including PCR (Polymerase Chain Reaction) or the like, in order to assess the activity, and in particular, level or abundance of a number of reference biomarkers. Quantified values indicative of the relative activity are then stored as part of the reference data.

In one example, the measurements are received as raw data, which then undergoes preliminary processing. Such raw data corresponds to information that has come from a source without modification, such as outputs from instruments such as PCR machines, array (e.g., microarray) scanners, sequencing machines, clinical notes or any other biochemical, biological, observational data, or the like. This step can be used to convert the raw data into a format that is better suited to analysis. In one example this is performed in order to normalize the raw data and thereby assist in ensuring the biomarker values demonstrate consistency even when measured using different techniques, different equipment, or the like. Thus, the goal of normalization is to remove the variation within the samples that is not directly attributable to the specific analysis under consideration. For example, to remove variances caused by differences in sample processing at different sites. Classic examples of normalization include z-score transformation for generic data, or popular domain specific normalizations, such as RMA normalization for microarrays.

However, it will also be appreciated that in some applications, such as a single sample experiment run on a single data acquisition machine, this step may not strictly be necessary, in which case the function can be a Null function producing an output identical to the input.

In one example, the preferred approach is a paired function approach over log normalized data. Log normalization is a standard data transformation on microarray data, because the data follow a log-normal distribution when coming off the machine. Applying a log transform turns the data into process-friendly normal data.

The individuals are selected to include individuals diagnosed with one or more conditions of interest, as well as healthy individuals. The conditions are typically medical, veterinary or other health status conditions and may include any illness, disease, stages of disease, disease subtypes, severities of disease, diseases of varying prognoses or the like, and in the current example would include at least some individuals with inSIRS and some individuals with ipSIRS. In this regard, the individuals also typically undergo a clinical assessment allowing the conditions to be clinically identified, and with an indication of any assessment or condition forming part of the reference data.

The biomarker values measured will depend on the predominant condition that is being assessed so, for example, in the case of determining the likelihood of a subject having inSIRS or ipSIRS, the biomarkers used will be LAMP1, CEACAM4, PLAC8 and PLA2G7, as discussed above.

Once collected, the reference data can be stored in the database 211 allowing this to be subsequently retrieved by the processing system 201 for subsequent analysis, or could be provided directly to the processing system 201 for analysis.

As part of the above process, at step 2410 the measurements are validated using traditional prior art techniques, to ensure that the measurements have been performed successfully, and hence are valid.

At step 2420, each individual with the reference population is typically allocated to a group. The groups may be defined in any appropriate manner and may be defined based on any one or more of an indication of a presence, absence, degree, stage, severity, prognosis or progression of a condition, other tests or assays, or measured biomarkers associated with the individuals.

For example, a first selection of groups may be to identify one or more groups of individuals suffering from SIRS, one or more groups of individuals suffering ipSIRS, and one or more groups of individuals suffering inSIRS. Further groups may also be defined for individuals suffering from other conditions. The groups may include overlapping groups, so for example it may be desirable to define groups of healthy individuals and individuals having SIRS, with further being defined to distinguish inSIRS patients from ipSIRS patients, as well as different degree of inSIRS or ipSIRS, with these groups having SIRS in common, but each group of patients differing in whether a clinician has determined the presence of an infection or not. Additionally, further subdivision may be performed based on characteristics of the individuals, phenotypic traits, measurement protocols or the like, so groups could be defined based on these parameters so that a plurality of groups of individuals suffering from a condition are defined, with each group relating to a different phenotypic trait, measurement protocol or the like.

It will also be appreciated, however, that identification of different groups can be performed in other manners, for example on the basis of particular activities of biomarkers within the biological samples of the reference individuals, and accordingly, reference to conditions is not intended to be limiting and other information may be used as required.

The manner in which classification into groups is performed may vary depending on the preferred implementation. In one example, this can be performed automatically by the processing system 201, for example, using unsupervised methods such as Principal Components Analysis (PCA), or supervised methods such as k-means or Self Organizing Map (SOM). Alternatively, this may be performed manually by an operator by allowing the operator to review reference data presented on a Graphical User Interface (GUI), and define respective groups using appropriate input commands.

At step 2430, first and second derived biomarker values are determined representing respective indicator values. The first and second indicator values In₁,In₂ are determined on a basis of ratios of concentrations of first and second, and third and fourth biomarkers respectively: In₁=(PLA2G7/PLAC8) In₂=(CEACAM4/LAMP1)

The indicator values are then used to establish indicator references at step 2440, which are then used in analyzing measured indicator values for a subject to establish a likelihood of the subject having a condition.

In particular, indicator values for each reference group are statistically analyzed to establish a range or distribution of indicator values that is indicative of each group, and an example distribution is shown in FIG. 26, as will be discussed in more detail below.

A further example will now be described with reference to FIGS. 25A and 25B.

In this example, at step 2500 a sample is acquired from the subject. The sample could be any suitable sample such as a peripheral blood sample, or the like, depending on the nature of the biomarker values being determined. At step 2505 the sample undergoes preparation allowing this to be provided to a measuring device and used in a quantification process at step 2510. For this purpose of this example, the quantification process involves PCR amplification, with the measuring device being a PCR machine, although other suitable techniques could be used. In this instance, amplifications times At(PLA2G7), At(PLAC8), At(CEACAM4), At(LAMP1) are determined for each of the four biomarkers at step 2515, with the amplification times being transferred from the measuring device to the processing system 201 allowing the processing system 201 to perform analysis of the corresponding biomarker values.

Accordingly, at step 2520 the processing system 201 calculates ratios using the amplifications times. In this regard, as the amplification times represent a log value, the ratios are determined by subtracting amplifications times as will be appreciated by a person skilled in the art.

Accordingly, in this example the indicator values would be determined as follows: In₁=At(PLA2G7)−At(PLAC8) In₂=At(CEACAM4)−At(LAMP1)

At step 2525 the processing system 201 determines an indicator value by combining the ratios for the indicator values, as follows: In=In₁+In₂

The processing system 201 then compares the indicator value to one or more respective indicator references at step 2530.

As previously described, the indicator references are derived for a reference population and are used to indicate the likelihood of a subject suffering from inSIRS or ipSIRS. To achieve this, the reference population is grouped based on a clinical assessment into groups having/not having the conditions or a measure of severity, risk or progression stage of the condition, with this then being used to assess threshold indicator values that can distinguish between the groups or provide a measure of severity, risk or progression stage.

The comparison is performed by comparing the indicator to an indicator distribution determined for each group in the reference population. In the current example, there are two reference groups, with one corresponding to individuals diagnosed with inSIRS and the other for individuals diagnosed with ipSIRS. In this instance, the results of the comparison can be used to determine a likelihood of the individual having ipSIRS as opposed to inSIRS. This can be achieved using a number of different methods, depending on the preferred implementation.

An example of a reference distribution is shown in FIG. 26, which shows the distribution of indicator values for a reference population containing both inSIRS and ipSIRS samples. The density (y axis) describes how common scores are in the reference population. In FIG. 26, the most common values for the inSIRS population are in the range 1 to 8, and for the ipSIRS population are mostly in the range 5 to 13. By way of example, let us assume that the calculated indicator value for a new sample is 4. A value of 4 in the inSIRS population has a high density at this value (A), while the ipSIRS population has a low density at this value (B), meaning that this sample is more likely to be inSIRS. Conversely, if a sample has an indicator score of 10, this value in the ipSIRS reference population has a high density (C), while the inSIRS population has a low density (D), meaning this it is more probable that this sample with an indicator value of 10 belongs to the ipSIRS population.

In practice this process can be performed by determining a basic probability based on score bands. For a given score band (i.e. 4-6), the proportion of individuals with SIRS or SEPSIS is calculated. For example, if 40% of the scores between 4 and 6 were SEPSIS, then if an subject has an indicator value between 4 and 6, they have a 40% probability of sepsis. Thus, for a given range within the reference distribution, the probability of belonging to one group or another (SIRS/SEPSIS) can simply be the proportion of that group within the range.

An alternative technique is a standard Bayes method. In this case, the technique uses a distribution of inSIRS scores, a distribution of ipSIRS scores and an indicator value for the subject. In this example, a standard score or equivalent is used to generate a probability of the indicator value belonging to the inSIRS distribution: pr(inSIRS) and separately to the ipSIRS distribution: pr(ipSIRS). The Bayes method is used to generate the probability of ipSIRS given the individual distributions.

Thus, given derived biomarker distributions for two or more groups (i.e. inSIRS/ipSIRS), the probability of membership for a single unknown sample into each distribution can be calculated (p-value) using for example a standard score (z-score). Then the p-values for each distribution can be combined into an overall probability for each class (i.e. inSIRS/ipSIRS) using for example Bayes rule or any other probability calculation method (including frequentist or empirical or machine learned methods).

Thus, once the indicator value has been derived and compared to the indicator distributions, the results of this comparison are used by the processing system 201 to calculate a likelihood of the subject having ipSIRS at step 2535, with this being used to generate a representation of the results at step 2540, which is provided for display at step 2545, for example to a clinician or medical practitioner. This can be achieved by displaying the representation on a client device, such as part of an email, dashboard indication or the like.

An example of the representation is shown in FIGS. 27A and 27B.

In this example, the representation 2700 includes a pointer 2710 that moves relative to a linear scale 2720. The linear scale is divided into regions 2721, 2722, 2723, 2724 which indicate whether the subject is suffering from level 1, 2, 3 or 4. Corresponding indicator number values are displayed at step 2730 with an indication of whether the corresponding value represents a likelihood of SIRS (inSIRS) or SEPSIS (ipSIRS) being shown at step 2740. An alphanumeric indication of the score is shown at step 2751 together with an associated probability of the biological subject having SEPSIS at step 2752.

As shown in this example, regions of the linear scale where the pointer is situated are highlighted with the diagnosis that is most unlikely being greyed out to make it absolutely clear where the subject sits on the scale. This results in a representation which when displayed at step 2545 is easy for a clinician to readily understand and to make a rapid diagnosis.

It will be appreciated from the above that a method can be provided for use in assessing the likelihood of a biological subject having inSIRS or ipSIRS the method including, in one or more processing devices:

-   -   a) determining a pair of biomarker values, the pair of biomarker         values being selected from the group consisting of:         -   i) a first pair of biomarker values indicative of a             concentration of polynucleotide expression products of the             PLA2G7 gene and PLAC8 gene;         -   ii) a second pair of biomarker values indicative of a             concentration of polynucleotide expression products of the             CEACAM4 gene and LAMP1 gene;     -   b) determining an indicator indicative of a ratio of the         concentrations of the polynucleotide expression products using         the pair of biomarker values;     -   c) retrieving previously determined first and second indicator         references from a database, the first and second indicator         references being determined based on indicators determined from         first and second groups of a reference population, one of the         groups consisting of individuals diagnosed with the medical         condition;     -   d) comparing the indicator to the first and second indicator         references;     -   e) using the results of the comparison to determine a         probability indicative of the subject having the medical         condition; and,     -   f) generating a representation of the probability, the         representation being displayed to a user to allow the user to         assess the likelihood of a biological subject having at least         one medical condition.

Similarly apparatus can be provided for determining the likelihood of a biological subject having inSIRS or ipSIRS, the apparatus including:

-   -   a) a sampling device that obtains a sample taken from a         biological subject, the sample including polynucleotide         expression products;     -   b) a measuring device that quantifies polynucleotide expression         products within the sample to determine a pair of biomarker         values, the pair of biomarker values being selected from the         group consisting of:         -   i) a first pair of biomarker values indicative of a             concentration of polynucleotide expression products of the             PLA2G7 gene and PLAC8 gene;         -   ii) a second pair of biomarker values indicative of a             concentration of polynucleotide expression products of the             CEACAM4 gene and LAMP1 gene;     -   c) at least one processing device that:         -   i) receives an indication of the pair of biomarker values             from the measuring device;         -   ii) determines an indicator using a ratio of the             concentration of the first and second polynucleotide             expression products using the biomarker values; and,         -   iii) compares the indicator to at least one indicator             reference; and,         -   iv) determines a likelihood of the subject having the at             least one medical condition using the results of the             comparison; and,         -   v) generates a representation of the indicator and the             likelihood for display to a user.

A further method that can be provided includes differentiating between inSIRS and ipSIRS in a biological subject, the method including:

-   -   a) obtaining a sample taken from a biological subject showing a         clinical sign of SIRS, the sample including polynucleotide         expression products;     -   b) in a measuring device:         -   i) amplifying at least some polynucleotide expression             products in the sample;         -   ii) determining an amplification amount representing a             degree of amplification required to obtain a defined level             of polynucleotide expression products including:             -   (1) amplification amounts for a first pair of                 polynucleotide expression products of the PLA2G7 gene                 and PLAC8 gene;             -   (2) amplification amounts for a second pair of                 polynucleotide expression products of the CEACAM4 gene                 and LAMP1 gene;     -   c) in a processing system:         -   i) retrieving the amplification amounts;         -   ii) determining an indicator by:             -   (1) determining a first derived biomarker value                 indicative of a ratio of concentrations of the first                 pair of polynucleotide expression products by                 determining a difference between the amplification                 amounts for the first pair;             -   (2) determining a second derived biomarker value                 indicative of a ratio of concentrations of the second                 pair of polynucleotide expression products by                 determining a difference between the amplification                 amounts for the second pair;             -   (3) determining the indicator by adding the first and                 second derived biomarker values;         -   iii) retrieving previously determined first and second             indicator references from a database, wherein the first and             second indicator references are distributions of indicators             determined for first and second groups of a reference             population, the first and second group consisting of             individuals diagnosed with inSIRS and ipSIRS respectively;         -   iv) comparing the indicator to the first and second             indicator references;         -   v) using the results of the comparison to determine a             probability of the subject being classified within the first             or second group;         -   vi) generating a representation at least partially             indicative of the indicator and the probability; and,         -   vii) providing the representation to a user to allow the             user to assess the likelihood of a biological subject having             at least one medical condition.

Additionally, a method can be provided for determining an indicator used in assessing a likelihood of a biological subject having a presence, absence, degree or prognosis of at least one medical condition, the method including:

-   -   a) determining a plurality of biomarker values, each biomarker         value being indicative of a value measured or derived for at         least one corresponding immune system biomarker of the         biological subject and being at least partially indicative of a         concentration of the immune system biomarker in a sample taken         from the subject;     -   b) determining the indicator using a combination of the         plurality of biomarker values, wherein:         -   i) at least two biomarkers have a mutual correlation in             respect of the at least one condition that lies within a             mutual correlation range, the mutual correlation range being             between ±0.9; and,         -   ii) the indicator has a performance value greater than or             equal to a performance threshold representing the ability of             the indicator to diagnose the presence, absence, degree or             prognosis of the at least one condition, the performance             threshold being indicative of an explained variance of at             least 0.3.

Throughout this specification and claims which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated integer or group of integers or steps but not the exclusion of any other integer or group of integers.

Persons skilled in the art will appreciate that numerous variations and modifications will become apparent. All such variations and modifications which become apparent to persons skilled in the art, should be considered to fall within the spirit and scope that the invention broadly appearing before described.

TABLE 1 HGNC^(§) Gene SEQ Name ID NO PRKCZ 1 SKI 2 RER1 3 TAS1R1 4 VAMP3 5 AGTRAP 6 VPS13D 7 KLHDC7A 8 NBL1//C1orf151 9 MDS2 10 RCAN3 11 LDLRAP1 12 MAN1C1 13 SH3BGRL3 14 DHDDS 15 HCRTR1 16 CCDC28B 17 LCK 18 ZNF362 19 THRAP3 20 PPIE//CCDC25 21 CAP1 22 CTPS 23 C1orf84 24 FAAH 25 DMBX1 26 CYP4B1 27 BTF3L4 28 LRRC42 29 C1orf175//TTC4 30 TMEM61 31 FPGT//TNNI3K 32 ACADM 33 SPATA1 34 EPHX4 35 RPAP2 36 RPL5//SNORA66//SNORD21// 37 FAM69A RTCD1 38 SLC30A7 39 RNPC3//AMY2B 40 CELSR2 41 AHCYL1 42 CEPT1//DRAM2 43 CHIA 44 LIX1L 45 UPF0627 46 MRPS21 47 TNFAIP8L2 48 SMCP 49 DCST1 50 RAG1AP1 51 C1orf182 52 HAPLN2 53 NTRK1 54 CD1E 55 TOMM40L//NR1I3 56 POU2F1 57 TIPRL 58 SFT2D2 59 CACNA1E 60 SMG7 61 OCLM 62 RGS2 63 ZC3H11A//RP11- 64 74E24.2 MFSD4 65 IL20 66 RPS6KC1 67 C1orf95 68 ARF1 69 GALNT2 70 TNFRSF4 71 NADK 72 FLJ14100//C1orf86 73 GPR153 74 RERE 75 SLC2A7 76 SDHB 77 RNF186 78 DDOST 79 GPN2 80 RPA2 81 PEF1 82 PTP4A2 83 TRIM62 84 PHC2 85 LSM10 86 MRPS15 87 RRAGC 88 COL9A2 89 TESK2 90 NRD1 91 KTI12 92 CC2D1B 93 YIPF1 94 JAK1 95 SLC35D1 96 DIRAS3 97 ZZZ3 98 GNG5 99 ZNHIT6 100 ODF2L 101 SEP15 102 BARHL2 103 GCLM 104 CLCC1//GPSM2//C1orf62 105 SORT1 106 SLC16A4 107 PHTF1 108 RSBN1 109 DENND2C//BCAS2 110 CD58 111 SPAG17//WDR3 112 REG4//NBPF7 113 RP11- 114 94I2.2//NBPF16//NBPF11// NBPF15//NBPF8// NBPF20//NBPF10// NBPF14//NBPF1// LOC100288142// NBPF12//KIAA1245// LOC100290137 APH1A 115 POGZ 116 TDRKH 117 THEM4 118 S100A11 119 CRNN 120 SPRR2C 121 S100A12 122 S100A8 123 GATAD2B//PLIN2 124 DENND4B 125 PBXIP1 126 PYGO2 127 SHC1 128 DCST2 129 GBA//GBAP 130 ASH1L 131 RIT1 132 MEF2D 133 AIM2 134 COPA 135 DEDD 136 TADA1L 137 GPA33 138 CD247 139 F5 140 PIGC 141 KIAA0040 142 TOR1AIP2//TOR1AIP1// 143 IFRG15 STX6//KIAA1614 144 EDEM3 145 UCHL5 146 DENND1B 147 DDX59 148 KIF21B 149 ARL8A 150 CYB5R1 151 MYBPH 152 CHI3L1 153 PIK3C2B//LOC100130573 154 NUAK2 155 NUCKS1 156 FAIM3 157 PLXNA2 158 SLC30A1 159 LPGAT1 160 ANGEL2 161 RAB3GAP2//AURKAPS1// 162 AURKA//SNORA36B TP53BP2 163 NVL 164 TMEM63A 165 PARP1 166 ITPKB 167 TARBP1 168 CHML 169 AKT3 170 SMYD3 171 AHCTF1 172 OR1C1 173 NCOA1 174 HADHB 175 ABHD1//PREB 176 SPAST 177 SLC30A6//DDX50 178 CRIPT 179 MSH2 180 FOXN2 181 CCDC104 182 VRK2 183 AHSA2//USP34 184 OTX1 185 AFTPH 186 CEP68 187 PLEK 188 ANXA4 189 MXD1 190 NAGK 191 SMYD5//NOTO 192 MTHFD2 193 TTC31 194 SEMA4F 195 TMSB10 196 SH2D6 197 GNLY 198 KCNIP3 199 CNNM4 200 CNNM3 201 ZAP70 202 LIPT1//MRPL30 203 MAP4K4 204 IL1R2 205 IL1R1 206 IL18R1 207 POLR1B 208 CHCHD5 209 IL1RN 210 PSD4 211 DDX18 212 INSIG2 213 TMEM177//LOC100125918 214 RALB 215 PROC 216 GPR17//LOC100291428// 217 LIMS2 IMP4 218 FAM123C 219 ACVR2A 220 MBD5 221 LYPD6B 222 SLC4A10 223 UBR3 224 HAT1 225 ITGA6 226 ZAK 227 OSBPL6 228 PLEKHA3 229 ZC3H15 230 COL3A1 231 GLS 232 OBFC2A 233 COQ10B 234 MARS2 235 CFLAR 236 NOP58 237 FAM117B 238 CYP20A1 239 FASTKD2 240 PIKFYVE 241 C2orf62 242 SLC11A1 243 AGFG1 244 CHRNG 245 EIF4E2 246 TRPM8 247 LRRFIP1 248 GAL3ST2 249 TMEM18 250 LAPTM4A 251 SF3B14 252 TP53I3 253 UNQ2999 254 GPR113//SELI 255 MPV17 256 PPM1G 257 NLRC4 258 CDC42EP3 259 HNRPLL 260 COX7A2L 261 KCNG3 262 CALM2//C2orf61 263 BCL11A 264 XPO1 265 NAT8B 266 DUSP11 267 MOGS 268 SNRNP200 269 SEMA4C 270 MITD1 271 IL1A 272 SLC35F5 273 CCDC93 274 CLASP1 275 SAP130 276 YSK4 277 GTDC1 278 ORC4L 279 NR4A2//FLJ46875 280 DPP4 281 GALNT3 282 SCN7A 283 FRZB 284 STK17B 285 CLK1//PPIL3 286 MPP4 287 INO80D 288 KLF7 289 FAM119A 290 NGEF 291 ARL4C 292 RAB17 293 HDLBP 294 LRRN1 295 SETD5 296 IRAK2 297 C3orf42 298 TSEN2 299 NR2C2//MRPS25 300 UBE2E1 301 C3orf35 302 SNRK 303 ZNF197 304 GNAI2 305 ALAS1 306 PRKCD 307 CACNA1D 308 PXK 309 PTPRG 310 ATXN7 311 SLC35A5 312 SLC15A2 313 CCDC48 314 DNAJC13 315 CLDN18 316 GYG1 317 SELT 318 MED12L 319 RAP2B 320 MYNN 321 ABCF3 322 VPS8 323 HRG 324 EIF4A2//SNORA4 325 LPP 326 CCDC50 327 LOC152217 328 TADA3L 329 SEC13 330 TIMP4 331 METTL6 332 DAZL//DAZ4//DAZ3// 333 DAZ2 SATB1//TBC1D5 334 SCN10A 335 SEC22C 336 ZDHHC3a 337 ZDHHC3b 338 SLC6A20 339 UQCRC1 340 PRKAR2A 341 IMPDH2 342 CCDC71 343 UBA7 344 CAMKV 345 WDR82 346 LMOD3 347 FOXP1 348 MORC1 349 ATG3 350 GSK3B//LOC100129275 351 HCLS1 352 KPNA1 353 PTPLB 354 C3orf22 355 RPN1 356 KIAA1257//ACAD9// 357 LOC100132731 FOXL2 358 MECOM 359 PLD1 360 GNB4 361 MRPL47 362 KLHL6 363 THPO 364 ETV5 365 BCL6//LOC100131635 366 ATP13A5 367 TMEM44 368 KIAA1530 369 TACC3 370 CNO 371 BST1 372 KLF3 373 TMEM33//DCAF4L1 374 KIT 375 ENAM 376 FAM47E//STBD1 377 ENOPH1 378 PDLIM5 379 CCDC109B//HIGD1A// 380 CCDC13 EGF 381 PCDH10 382 RAB33B 383 TMEM184C 384 RBM46 385 GRIA2 386 C4orf39 387 KLHL2 388 TLL1 389 F11 390 SLBP 391 HAUS3//POLN 392 PPARGC1A 393 TLR10 394 C4orf34 395 TXK 396 RPL21P44 397 KDR 398 RCHY1 399 CNOT6L 400 PLAC8 401 HPSE 402 GPRIN3 403 PPA2 404 COL25A1 405 C4orf3 406 QRFPR 407 MFSD8 408 MAP9 409 PDGFC 410 TKTL2 411 ACSL1 412 SUB1//TMEM183A 413 CARD6 414 MCCC2 415 TNPO1 416 PDE8B 417 PAPD4 418 THBS4 419 FAM151B 420 RASGRF2 421 SNX2 422 LMNB1//PCIF1 423 MEGF10 424 LEAP2 425 TCF7 426 KDM3B 427 CXXC5 428 SLC4A9 429 ANKHD1- 430 EIF4EBP3//ANKHD1// EIF4EBP3 KIAA0141 431 GRPEL2 432 MFAP3 433 GABRA6 434 GABRA1 435 DOCK2 436 RANBP17//USP12 437 ERGIC1 438 ATP6V0E1//SNORA74B 439 ZNF346 440 NSD1 441 CLPTM1L 442 UGT3A1 443 GDNF 444 TTC33 445 hCG_2039148 446 MOCS2 447 SLC38A9 448 CCDC125 449 ANKRA2 450 HAPLN1 451 CCNH 452 TMEM161B 453 MBLAC2 454 MCTP1 455 TICAM2//TMED7//TMED7- 456 TICAM2 KIF3A 457 C5orf15 458 SKP1 459 CXCL14 460 KLHL3 461 CD14 462 YIPF5 463 LARS 464 DCTN4 465 CCDC69 466 ATOX1 467 TIMD4 468 ADAM19 469 SLIT3 470 RNF44 471 DOK3 472 MGAT4B//SQSTM1 473 C5orf45//SQSTM1 474 RASGEF1C 475 MGAT1 476 IRF4 477 HIVEP1 478 E2F3 479 HIST1H4I 480 HIST1H2BM 481 MOG 482 ZNRD1//NCRNA00171 483 TRIM15 484 HCG27 485 BAT2//SNORA38 486 CYP21A2 487 ITPR3 488 MAPK14 489 MAPK13 490 PNPLA1 491 SFRS3 492 CDKN1A 493 FOXP4 494 CUL9 495 RUNX2 496 ZNF451 497 SOBP 498 C6orf182 499 KIAA1919 500 RWDD1 501 KPNA5 502 TPD52L1 503 ARG1 504 RAB32 505 ARID1B 506 SLC22A3 507 SERPINB1 508 C6orf146 509 GCM2 510 ATXN1 511 DCDC2//KAAG1 512 HIST1H3I 513 HIST1H4L 514 GABBR1 515 RNA243 516 DDAH2 517 CLIC1 518 NEU1 519 RXRB 520 VPS52 521 TCP11 522 CLPS 523 PGC 524 ZNF318 525 YIPF3 526 MRPL14 527 PLA2G7 528 PKHD1 529 IL17F 530 HTR1B 531 GABRR2 532 UBE2J1 533 BACH2 534 MCM9 535 VNN1 536 IL20RA 537 FLJ27255 538 T 539 RPS6KA2 540 HGC6.3 541 UNC84A//C7orf20 542 SDK1 543 ZDHHC4 544 C7orf26 545 GLCCI1//tcag7.903 546 GPNMB 547 CCDC126 548 WIPF3//ZNRF2//LOC441208 549 GPR141 550 STARD3NL 551 POU6F2 552 CDC2L5 553 ZMIZ2 554 UPP1 555 ZNF273 556 KCTD7//RABGEF1 557 RABGEF1//tcag7.967// 558 tcag7.951//KCTD7// LOC100293333 CCDC132 559 PVRIG//PILRB//STAG3 560 PILRB//PVRIG//STAG3 561 C7orf51 562 GNB2 563 LRRC17 564 LRRN3 565 CFTR 566 LSM8 567 LUC7L2 568 MGAM//LOC100124692 569 GIMAP7 570 INSIG1 571 RBM33 572 ICA1 573 FAM126A 574 HIBADH 575 TRIL 576 SCRN1 577 ELMO1 578 INHBA 579 CAMK2B 580 NPC1L1 581 DDC//LOC100129427 582 NSUN5//NSUN5B// 583 NSUN5C CLDN3 584 C7orf23//DMTF1 585 SRI 586 BET1 587 MCM7 588 GATS 589 ATXN7L1//RINT1// 590 EFCAB10 KIAA1549 591 SLC37A3 592 SMARCD3 593 MLL3//BAGE2 594 CLN8 595 MSRA 596 PIWIL2 597 NEFM//LOC100129717 598 EPHX2 599 LEPROTL1 600 MAK16//C8orf41 601 AP3M2 602 FNTA 603 SGK196 604 UBE2V2 605 FLJ46365 606 SNTG1 607 TRIM55 608 C8orf45 609 PREX2 610 PLEKHF2 611 BAALC//FLJ10489 612 TTC35 613 MTBP 614 ZHX2 615 RNF139 616 TG 617 DENND3//C8orf60 618 TNFRSF10D 619 TRIM35 620 GSR 621 WHSC1L1 622 PCMTD1//PXDNL 623 NCOA2 624 TRAM1//LOC286190 625 RUNX1T1 626 EXT1 627 DDEF1IT1 628 CDC37L1 629 UBE2R2 630 UBAP1//KIF24 631 GALT 632 RGP1//GBA2 633 TGFBR1 634 C9orf6//IKBKAP 635 IMAGE5303689 636 ATP6V1G1 637 TLR4 638 SET 639 MRPL41 640 C9orf68 641 HAUS6//SCARNA8 642 KLHL9 643 C9orf82 644 NDUFB6//DFFB 645 SIT1 646 FAM108B1 647 TRPM6 648 FRMD3 649 SLC28A3 650 BICD2 651 C9orf84 652 AKNA 653 MEGF9 654 C5 655 GOLGA1//SCAI 656 SH2D3C 657 FAM102A 658 FLJ10232 659 ASB6 660 BAT2L 661 EDF1 662 FBXW5 663 C10orf18 664 FBXO18 665 GATA3 666 CUGBP2 667 VIM 668 STAM 669 WAC 670 BAMBI 671 ZNF487//LOC439911 672 ALOX5 673 WDFY4 674 SRGN 675 CCDC109A 676 FAM149B1//FAM149B2 677 MINPP1 678 PTEN//PTENP1 679 ENTPD1//C10orf131 680 ABCC2 681 SFXN2 682 SHOC2 683 ACSL5 684 BCCIP//DHX32 685 FAM188A 686 CUBN 687 SVIL//hCG_1783494 688 FAM13C//PHYHIPL 689 ATAD1 690 ANKRD22 691 FLJ34077 692 COX15 693 ERLIN1 694 ACTR1A 695 ABLIM1 696 RAB11FIP2 697 C10orf84 698 PRDX3 699 C10orf119 700 NSMCE4A 701 TALDO1//INTS8 702 TNNT3 703 FXC1 704 PDE3B 705 DNAJC24 706 PTPRJ//OR4B1 707 C11orf31 708 TMEM109 709 CD6 710 CD5 711 TMEM138 712 POLR2G 713 TMEM179B 714 NAT11 715 OTUB1 716 RBM14//RBM4 717 AIP 718 PPFIA1 719 IL18BP//NUMA1 720 C11orf30 721 C11orf82 722 TMEM126B 723 C11orf73 724 PIWIL4 725 LOC100132686 726 PAFAH1B2 727 UBE4A 728 TRAPPC4 729 SC5DL 730 VWA5A//OR10D1P 731 STT3A 732 VPS26B 733 TRIM21 734 ZBED5 735 SAAL1 736 FANCF 737 LIN7C 738 PHF21A 739 CUGBP1 740 OSBP 741 CYBASC3 742 TUT1 743 SLC25A45 744 LTBP3 745 EIF1AD 746 GAB2 747 CREBZF 748 PICALM 749 SLC36A4 750 CCDC82 751 KIAA1826 752 MPZL3 753 MPZL2 754 H2AFX 755 SIAE 756 ZBTB44 757 HSN2 758 ADIPOR2 759 NCAPD2//SCARNA10// 760 FADS1 PTPN6 761 CLEC4D 762 CDKN1B 763 GOLT1B 764 FAR2 765 FGD4 766 TMEM106C 767 TMBIM6 768 C12orf62 769 PRR13//PCBP2 770 DGKA 771 COQ10A 772 TSPAN31 773 CDK4/MARCH9/C3HC4 774 LEMD3 775 IRAK3 776 TMTC3 777 ACTR6 778 TCTN1 779 PXMP2//PGAM5 780 DCP1B 781 SLC2A3//SLC2A14 782 C3AR1 783 PLBD1 784 TM7SF3 785 ASB8//PHB 786 LMBR1L 787 FMNL3//PRPF40B 788 AAAS 789 NFE2 790 GPR84 791 CD63 792 SARNP//DNAJC14 793 NACA 794 CDK4//TSPAN31 795 TMBIM4//LOC100133322 796 IL22 797 LIN7A 798 HAL 799 APPL2 800 GLTP 801 GIT2 802 VPS29 803 PPTC7 804 DDX54//CCDC42B 805 SLC24A6 806 SDS 807 RBM19 808 MED13L 809 C12orf49 810 FBXO21 811 WSB2 812 TAOK3 813 CIT 814 RAB35 815 RPLP0 816 PXN 817 TRIAP1 818 SFRS9 819 POP5 820 UNQ1887 821 C12orf43 822 ANAPC5 823 KDM2B 824 MORN3 825 TMEM120B//RHOF 826 LOC338799 827 DIABLO//B3GNT4 828 VPS33A 829 CLIP1 830 PITPNM2 831 EIF2B1 832 CCDC92 833 NCOR2 834 DHX37 835 DDX51 836 POLE 837 GOLGA3 838 ZMYM2 839 SPATA13//C1QTNF9 840 NUPL1 841 PAN3//EEF1A1//CHCHD2 842 ALOX5AP 843 EEF1DP3 844 KL 845 UFM1 846 NARG1L 847 ITM2B 848 FNDC3A 849 CDADC1 850 ARL11 851 LMO7 852 DNAJC3 853 TM9SF2 854 CLYBL 855 PCCA 856 ABHD13 857 LAMP1 858 TMCO3 859 UPF3A 860 ZMYM5//ZMYM2 861 ZDHHC20//LOC728099 862 PARP4 863 MTMR6//LOC646482 864 HSPH1 865 N4BP2L2//CG030 866 ELF1 867 LCP1 868 KPNA3 869 C13orf1 870 DLEU2//DLEU2L 871 GUCY1B2 872 INTS6 873 DACH1 874 TBC1D4 875 EDNRB 876 UGGT2 877 GPR183 878 LIG4 879 ANKRD10 880 RASA3 881 RNASE2//LOC643332 882 RPGRIP1 883 IRF9 884 TSSK4 885 C14orf21 886 SCFD1 887 FANCM 888 ABHD12B 889 PTGDR 890 FBXO34//KIAA0831 891 C14orf101 892 ACTR10 893 ARID4A 894 JKAMP 895 HIF1A 896 SYNE2 897 EXD2 898 SLC39A9 899 SFRS5 900 PCNX 901 SIPA1L1//SNORD56B// 902 LOC145474//LOC283567 YLPM1 903 BATF 904 FLVCR2//RPS24 905 GPR65 906 TDP1 907 EVL 908 ZNF839 909 TDRD9 910 INF2 911 PLD4 912 MTA1//LOC647310// 913 LOC100128343 NDRG2 914 DAD1//OR6J1 915 SLC7A8 916 IPO4 917 TM9SF1 918 ADCY4 919 RIPK3 920 EAPP 921 BAZ1A 922 NFKBIA 923 SEC23A 924 C14orf104 925 C14orf138 926 SOS2 927 NIN 928 PYGL 929 CNIH 930 DHRS7 931 WDR89 932 ACTN1 933 NUMB 934 C14orf43 935 ABCD4 936 KIAA0317 937 NEK9 938 ANGEL1 939 SPTLC2 940 SERPINA6 941 DICER1 942 BCL11B 943 ANKRD9 944 PPP1R13B 945 AKT1 946 BRF1 947 TUBGCP5 948 SNRPN* 949 APBA2 950 MTMR15//MTMR10 951 RYR3 952 BAHD1 953 CHP 954 JMJD7- 955 PLA2G4B//JMJD7// PLA2G4B HAUS2 956 C15orf63//SERF2 957 B2M 958 TRIM69 959 PLDN 960 SQRDL 961 GALK2 962 USP8 963 GLDN 964 MAPK6 965 LACTB 966 RAB8B 967 APH1B 968 USP3//LOC100130855 969 SNX1 970 LBXCOR1//PIAS1// 971 CALML4 NEO1 972 MPI 973 FBXO22//FBXO22OS 974 RCN2 975 FAH 976 IL16 977 ABHD2 978 SLCO3A1 979 MCTP2 980 MEF2A//LYSMD4 981 NIPA2//CYFIP1 982 HERC2//HERC2P2// 983 HERC2P3//LOC440248 MTMR10//MTMR15 984 C15orf24 985 SLC12A6 986 LPCAT4 987 INO80 988 OIP5 989 ZFP106 990 CDAN1 991 SPG11//ISLR 992 SPPL2A 993 GNB5//LOC100129973 994 MYO5A 995 ARPP19 996 RAB27A 997 CCPG1//PIGB//DYX1C1 998 BNIP2 999 CA12 1000 FAM96A 1001 KIAA0101//CSNK1G1 1002 TLE3 1003 PARP6 1004 NPTN 1005 MAN2C1 1006 IMP3 1007 MTHFS 1008 ST20//C15orf37 1009 TMC3 1010 AP3B2 1011 C15orf40 1012 WDR73 1013 NTRK3 1014 DET1 1015 TM2D3 1016 WDR90 1017 RHOT2//FBXL16 1018 TMEM204 1019 CRAMP1L//HN1L 1020 MAPK8IP3 1021 TBL3 1022 TSC2 1023 KCTD5//PRO0461// 1024 PDPK1 CLUAP1 1025 DNASE1 1026 DNAJA3 1027 CP110 1028 C16orf62 1029 LYRM1 1030 METTL9 1031 EEF2K 1032 POLR3E 1033 PLK1 1034 PRKCB 1035 IL21R//LOC283888 1036 SULT1A2//SULT1A1 1037 ATXN2L 1038 LAT^(¶) 1039 KIF22 1040 MAZ 1041 CORO1A//LOC606724 1042 ITGAL 1043 SRCAP//SNORA30 1044 ZNF646//ZNF668 1045 C16orf67 1046 TMEM188 1047 LPCAT2 1048 CETP 1049 CKLF 1050 CMTM1//CKLF 1051 TMEM208 1052 CTCF 1053 THAP11 1054 NUTF2 1055 EDC4 1056 SLC7A6//SLC7A6OS 1057 PRMT7 1058 SNTB2 1059 VPS4A 1060 DDX19B//DDX19A 1061 CHST4 1062 HP//HPR 1063 PLCG2 1064 KLHL36 1065 KIAA0182 1066 BANP//RUNDC2C 1067 TRAPPC2L 1068 SPG7 1069 CDK10 1070 TCF25 1071 AFG3L1 1072 LUC7L 1073 AXIN1 1074 JMJD8 1075 LMF1 1076 UNKL 1077 UNKL 1078 CLCN7 1079 MRPS34 1080 RNPS1 1081 NLRC3 1082 TRAP1//DNASE1 1083 ADCY9 1084 CORO7 1085 C16orf72 1086 RRN3//LOC653390// 1087 LOC730092//LOC100131998 XYLT1//LYRM2//ZC3H11A 1088 DCUN1D3//LYRM1 1089 IGSF6//METTL9 1090 CDR2//RRN3//LOC100131998// 1091 LOC653390 COG7 1092 GGA2 1093 NSMCE1 1094 GTF3C1 1095 CCDC101//LOC388242 1096 C16orf54 1097 KCTD13 1098 SEPT1 1099 ZNF764//ZNF747 1100 C16orf58//LOC100128371 1101 ITFG1 1102 ABCC11//LONP2 1103 NUDT21 1104 BBS2//OGFOD1 1105 CSNK2A2 1106 GOT2 1107 FAM96B 1108 FHOD1//SLC9A5 1109 ATP6V0D1//LOC100132855 1110 GFOD2 1111 SLC12A4 1112 DPEP3 1113 DPEP2 1114 CHTF8//HAS3 1115 COG8//PDF 1116 TERF2 1117 AARS 1118 ST3GAL2 1119 VAC14//LOC100130894 1120 AP1G1 1121 WDR59 1122 CTRB2//CTRB1 1123 TAF1C//ADAD2 1124 FBXO31 1125 ZCCHC14 1126 FAM38A 1127 CENPBD1 1128 TIMM22 1129 RPA1 1130 DPH1//OVCA2 1131 SGSM2 1132 ARRB2 1133 LOC100130950 1134 DNAH2 1135 PIGL 1136 TRPV2 1137 MPRIP 1138 DRG2 1139 ALKBH5//FLJ13773 1140 SMCR7 1141 WSB1 1142 TAOK1 1143 CPD 1144 SUZ12P 1145 RNF135 1146 ZNF830 1147 TAF15 1148 GGNBP2 1149 LASP1 1150 PSMD3 1151 CDC6 1152 NBR2 1153 TMUB2 1154 MGC57346//C17orf69 1155 NSF//LOC728806 1156 GOSR2 1157 NPEPPS//TBC1D3F// 1158 LOC440434 KPNB1 1159 CDK5RAP3 1160 ATP5G1 1161 UBE2Z 1162 XYLT2//LOC100130580 1163 NOG 1164 DGKE 1165 AKAP1 1166 TMEM49//CLTC//MIR21 1167 CLTC 1168 CA4 1169 C17orf64 1170 DCAF7 1171 PITPNC1 1172 NOL11//SNORA38B 1173 MAP2K6 1174 COG1 1175 CD300A 1176 TMEM104 1177 MRPS7 1178 KIAA0195 1179 TSEN54 1180 LLGL2 1181 LOC100134934//CDK3 1182 MFSD11 1183 SEPT9 1184 TNRC6C 1185 TMC8 1186 ENGASE 1187 RPTOR 1188 GPS1 1189 FN3KRP 1190 TBCD 1191 GEMIN4 1192 GLOD4 1193 SLC43A2 1194 PRPF8 1195 SMG6//C17orf6 1196 METT10D//LOC284009 1197 SHPK 1198 TAX1BP3 1199 P2RX5 1200 MYBBP1A//SPNS2 1201 PELP1 1202 PFN1 1203 ZNF232 1204 DHX33 1205 DERL2 1206 NLRP1//LOC728392 1207 ASGR2 1208 NEURL4//GPS2//D4S234E 1209 ZBTB4 1210 TP53 1211 VAMP2 1212 PIK3R5 1213 ELAC2 1214 NCOR1//C20orf191// 1215 LOC100131704 ZNF287 1216 TOM1L2//LOC246315 1217 GRAP//SNORD3B- 1218 1//SNORD3B- 2//LOC400581 ALDOC 1219 SDF2 1220 RAB34 1221 PHF12 1222 NUFIP2 1223 OMG 1224 EVI2B 1225 C17orf66//RSL24D1 1226 SYNRG//LOC100131822 1227 PLXDC1 1228 CACNB1 1229 PGAP3 1230 MED24 1231 NR1D1//THRA 1232 CCR7 1233 STAT5B//STAT5A 1234 FAM134C 1235 VAT1 1236 DUSP3 1237 C17orf65//ASB16 1238 UBTF 1239 GPATCH8 1240 MAP3K14//LOC100133991 1241 OSBPL7 1242 SLC35B1 1243 TOB1 1244 COX11//TOM1L1 1245 VEZF1 1246 SFRS1//FLJ44342 1247 SEPT4 1248 MED13//LOC100129112 1249 LIMD2//MAP3K3 1250 STRADA 1251 FTSJ3 1252 CD79B 1253 ICAM2 1254 ERN1 1255 TEX2 1256 LRRC37A3//LRRC37A2// 1257 LRRC37A//ARL17P1// LRRC37A4// LOC100294335//LOC644397 GNA13 1258 WIPI1//ARSG 1259 FAM20A 1260 NAT9 1261 GGA3 1262 H3F3B//H3F3C 1263 EXOC7 1264 SFRS2 1265 TMC6//LOC100131096 1266 USP36 1267 CD7 1268 RAB31 1269 VAPA 1270 SEH1L 1271 HQ0644/PRO0644 1272 RNMT 1273 RNF138 1274 GALNT1 1275 ELP2 1276 PIK3C3 1277 SLC14A2 1278 ME2 1279 SERPINB2//SERPINB10 1280 ZNF407 1281 ZNF236 1282 NFATC1//LOC100127994 1283 ENOSF1//TYMS 1284 MYOM1 1285 AFG3L2 1286 ABHD3 1287 OSBPL1A 1288 CDH2 1289 DSC1 1290 PSTPIP2 1291 C18orf32 1292 MBD2//SNORA37 1293 PIGN 1294 TMX3 1295 PQLC1 1296 GZMM 1297 ARID3A 1298 CIRBP 1299 DAZAP1 1300 SPPL2B 1301 NFIC 1302 VAV1 1303 ARHGEF18//LOC100128573 1304 STXBP2//LOC554363// 1305 LOC100131801 C19orf59 1306 ZNF317 1307 ILF3 1308 SMARCA4 1309 PRKCSH 1310 IER2 1311 CCDC130 1312 DCAF15 1313 IL27RA 1314 KLF2 1315 SIN3B 1316 DDA1 1317 GTPBP3 1318 FAM129C 1319 FCHO1 1320 ARRDC2 1321 IFI30 1322 C19orf60 1323 CRTC1//MAML2 1324 RFXANK//MEF2B//LOC729991 1325 ZNF101 1326 ZNF738 1327 ZNF257//ZNF492// 1328 ZNF99//ZNF98//LOC646864 C19orf2 1329 KIAA0355//FLJ21369 1330 USF2 1331 TMEM147 1332 LIN37//PSENEN 1333 C19orf55 1334 TBCB//POLR2I 1335 ZNF382 1336 ZNF568 1337 ZNF420 1338 ZNF383 1339 CCDC97 1340 ZNF574 1341 CD177 1342 ZNF230//ZNF222 1343 VASP 1344 GRWD1 1345 FLT3LG 1346 ZNF175 1347 NCRNA00085 1348 PPP2R1A 1349 ZNF808//ZNF578// 1350 ZNF611 LENG8 1351 FCAR 1352 RPL28 1353 U2AF2 1354 LOC100288114//MGC9913 1355 ZFP28 1356 ZNF460 1357 ZNF549 1358 ZNF211 1359 ZNF587//ZNF417 1360 ZNF274 1361 ZNF544 1362 ZNF8 1363 TRIM28 1364 C19orf6 1365 C19orf34 1366 GNG7 1367 AES 1368 EEF2//SNORD37 1369 PLIN5//LRG1 1370 PLIN3 1371 PTPRS 1372 SAFB2//SAFB 1373 RANBP3 1374 GTF2F1//LOC100130856 1375 XAB2 1376 ELAVL1 1377 ADAMTS10 1378 FBXL12 1379 DNMT1 1380 TYK2 1381 KEAP1 1382 KRI1 1383 TMEM205//hCG_29977 1384 ZNF563 1385 MAN2B1//MORG1 1386 C19orf56 1387 DHPS 1388 TNPO2//SNORD41 1389 LPHN1 1390 NDUFB7 1391 AKAP8 1392 AKAP8L 1393 CHERP//C19orf44// 1394 CALR3 INSL3//JAK3 1395 IL12RB1 1396 UPK1A 1397 TYROBP 1398 ZNF529 1399 ZNF461 1400 ZNF607 1401 YIF1B 1402 PRR13 1403 CEACAM4 1404 PLAUR 1405 TRAPPC6A 1406 ERCC1//CD3EAP 1407 RTN2 1408 SYMPK 1409 PGLYRP1 1410 NOSIP 1411 PNKP 1412 NKG7 1413 FPR1 1414 ZNF28 1415 OSCAR 1416 MBOAT7 1417 LILRA5 1418 LILRA4 1419 ZNF550//ZNF549 1420 ZNF416 1421 ZNF256 1422 ZNF329 1423 FAM110A 1424 ITPA 1425 CDC25B 1426 CDS2 1427 CRLS1 1428 CSRP2BP 1429 SEC23B 1430 SLC24A3 1431 HCK 1432 ASXL1 1433 ACSS2 1434 C20orf4 1435 TGIF2 1436 C20orf24//SLA2 1437 RPN2//EEF1A2 1438 CTNNBL1 1439 ACTR5 1440 PPP1R16B 1441 DHX35 1442 PLCG1 1443 MYBL2 1444 SYS1//SYS1- 1445 DBNDD2//DBNDD2 DNTTIP1 1446 CTSA 1447 MMP9//LOC100128028 1448 DDX27 1449 SLC9A8 1450 RNF114 1451 PTPN1 1452 TSHZ2 1453 PFDN4 1454 CSTF1 1455 CASS4 1456 GNAS 1457 C20orf177 1458 CDH26 1459 C20orf197 1460 LOC284757 1461 ARFGAP1 1462 PRPF6 1463 NSFL1C 1464 SIRPD 1465 SIRPG//SIRPA 1466 RNF24 1467 RASSF2 1468 TMX4 1469 JAG1 1470 C20orf74 1471 C20orf3 1472 C20orf112 1473 CDK5RAP1 1474 AHCY 1475 GGT7 1476 EDEM2 1477 RBM39//LOC643167 1478 BLCAP 1479 SERINC3//TTPAL 1480 ZNF335 1481 ELMO2 1482 B4GALT5 1483 DPM1 1484 ZFP64 1485 ZNF217 1486 CTSZ 1487 SYCP2 1488 PSMA7 1489 DIDO1 1490 YTHDF1 1491 CHODL 1492 BACH1 1493 C21orf41//BACH1 1494 IL10RB 1495 IFNAR1 1496 IFNGR2 1497 SON 1498 MORC3//DOPEY2 1499 DYRK1A 1500 KCNJ15 1501 ETS2 1502 RRP1B 1503 PFKL 1504 TRPM2 1505 ADARB1 1506 SAMSN1//LOC388813 1507 N6AMT1 1508 SYNJ1 1509 TMEM50B 1510 KCNE1 1511 PRDM15 1512 C2CD2 1513 WDR4 1514 U2AF1 1515 CSTB 1516 UBE2G2//SUMO3 1517 PTTG1IP 1518 POFUT2 1519 MCM3AP 1520 IL17RA//CECR7 1521 C22orf37 1522 LZTR1 1523 PPIL2//YPEL1 1524 CYTSA 1525 SNRPD3//C22orf13 1526 NF2 1527 LIMK2 1528 SLC5A1 1529 MCM5 1530 NCF4 1531 GGA1 1532 SH3BP1//PDXP 1533 POLR2F//LOC100131530 1534 APOBEC3A//APOBEC3B 1535 APOBEC3D 1536 ATF4 1537 CACNA1I 1538 ZC3H7B 1539 CCDC134 1540 TSPO 1541 NUP50 1542 TBC1D22A//LOC100289878 1543 RP3-402G11.5 1544 SAPS2 1545 NCAPH2 1546 BID 1547 SLC25A1 1548 KLHL22//KRT18 1549 PI4KA//PI4KAP1//PI4KAP2// 1550 LOC100293141 MAPK1 1551 ZNF70 1552 TPST2 1553 SF3A1//CCDC157 1554 PES1 1555 PIK3IP1 1556 PATZ1 1557 C22orf30 1558 IL2RB 1559 CSNK1E//LOC400927 1560 UNC84B 1561 CBX7//LOC100128400 1562 RPS19BP1 1563 MKL1//KIAA1659 1564 RANGAP1 1565 TCF20 1566 LDOC1L 1567 UNQ6126 1568 TUBGCP6 1569 SBF1//SBF1P1 1570 MSL3 1571 MOSPD2 1572 BMX//HNRPDL 1573 PDHA1 1574 YY2 1575 PDK3 1576 GK//GK3P//FTL//LOC652904 1577 CXorf59 1578 ATP6AP2 1579 USP9X//USP9Y 1580 RP2 1581 USP11 1582 RBM3 1583 FTSJ1 1584 WAS 1585 PLP2 1586 TSPYL2//GPR173 1587 MAGED2 1588 UBQLN2 1589 NLGN3 1590 ACRC 1591 UPRT 1592 CXorf26 1593 ATP7A 1594 DIAPH2 1595 CSTF2//RAD21 1596 ARMCX3 1597 ARMCX5 1598 GPRASP1 1599 TMEM31 1600 TBC1D8B 1601 MID2 1602 DOCK11 1603 LONRF3 1604 UBE2A 1605 SH2D1A 1606 OCRL 1607 SLC25A14 1608 HPRT1 1609 CD40LG 1610 AFF2 1611 SSR4//IDH3G 1612 FAM50A 1613 DKC1//SNORA36A// 1614 SNORA56 ARSD 1615 KAL1 1616 CTPS2 1617 RPS6KA3 1618 BCOR 1619 MAOB//NAT13 1620 ZNF41 1621 OTUD5 1622 KCND1 1623 ZMYM3 1624 MAGT1 1625 BRWD3 1626 TRMT2B 1627 GLA 1628 MORF4L2 1629 PSMD10 1630 ACSL4 1631 LAMP2 1632 CUL4B 1633 ODZ1 1634 ELF4 1635 RAP2C 1636 FAM127B//FAM127C// 1637 FAM127A TMEM185A 1638 ARD1A 1639 IRAK1 1640 DNASE1L1//RPL10 1641 SH3KBP1 1642 Mitochondrial1 1643 Mitochondrial2 1644 CCNL2 1645 INPP5B 1646 TLR5 1647 ADRB3//GOT1L1 1648 NOC2L//SAMD11// 1649 LOC401010 SHFM1 1650 ^(§) HUGO Gene Nomenclature Committee ^(¶) Synonymous with SPNS1//NPIPL2//LOC728741//LOC730153//NPIPL3//SPIN1//LOC728888//LOC100289169//LOC728734//LOC729602//LOC100288442//LOC100288332 *Synonymous with SNURF//IPW//SNORD116-16//SNORD116-18//SNORD116-21//SNORD116-22//SNORD116-17//SNORD116-19//PAR5//PAR-SN//SNORD116-2//SNORD116-25//SNORD116-26//SNORD107//SNORD115-12//SNORD115-5//SNORD115-6//SNORD115-9//SNORD116-11//SNORD116-12//SNORD116-13//SNORD116-28//SNORD116-4//SNORD64//PAR1//SNORD109A//SNORD109B//SNORD116-6//SNORD116-3//SNORD116-9//SNORD115-13//SNORD115-1//SNORD115-14//SNORD115-15//SNORD115-21//SNORD115-10//SNORD115-7//SNORD115-16//SNORD115-40//SNORD115-42//SNORD115- 11//SNORD115-29//SNORD115-34//SNORD115-36//SNORD115-4//SNORD115-43//HBII-52-24//SNORD116-5//SNORD116-7//SNORD115-26//SNORD115-30//SNORD116-15//SNORD116-8//SNORD115-2//SNORD115-39//SNORD116-14//SNORD116-20//SNORD115-8//SNORD115-3//SNORD115-38//SNORD115-41//SNORD115-22//SNORD115-44//SNORD116-1//SNORD115-17//SNORD115-18//SNORD115-19//SNORD115-20//SNORD116@

TABLE 2 HGNC^(§) Gene SEQ Name ID NO PRKCZ 1651 SKI 1652 RER1 1653 TAS1R1 1654 VAMP3 1655 AGTRAP 1656 VPS13D 1657 KLHDC7A 1658 NBL1//C1orf151 1659 MDS2 1660 RCAN3 1661 LDLRAP1 1662 MAN1C1 1663 SH3BGRL3 1664 DHDDS 1665 HCRTR1 1666 CCDC28B 1667 LCK 1668 ZNF362 1669 THRAP3 1670 PPIE//CCDC25 1671 CAP1 1672 CTPS 1673 C1orf84 1674 FAAH 1675 DMBX1 1676 CYP4B1 1677 BTF3L4 1678 LRRC42 1679 C1orf175//TTC4 1680 TMEM61 1681 FPGT//TNNI3K 1682 ACADM 1683 SPATA1 1684 EPHX4 1685 RPAP2 1686 RPL5//SNORA66// 1687 SNORD21//FAM69A RTCD1 1688 SLC30A7 1689 RNPC3//AMY2B 1690 CELSR2 1691 AHCYL1 1692 CEPT1//DRAM2 1693 CHIA 1694 LIX1L 1695 UPF0627 1696 MRPS21 1697 TNFAIP8L2 1698 SMCP 1699 DCST1 1700 RAG1AP1 1701 C1orf182 1702 HAPLN2 1703 NTRK1 1704 CD1E 1705 TOMM40L//NR1I3 1706 POU2F1 1707 TIPRL 1708 SFT2D2 1709 CACNA1E 1710 SMG7 1711 OCLM 1712 RGS2 1713 ZC3H11A//RP11- 1714 74E24.2 MFSD4 1715 IL20 1716 RPS6KC1 1717 C1orf95 1718 ARF1 1719 GALNT2 1720 TNFRSF4 1721 NADK 1722 FLJ14100//C1orf86 1723 GPR153 1724 RERE 1725 SLC2A7 1726 SDHB 1727 RNF186 1728 DDOST 1729 GPN2 1730 RPA2 1731 PEF1 1732 PTP4A2 1733 TRIM62 1734 PHC2 1735 LSM10 1736 MRPS15 1737 RRAGC 1738 COL9A2 1739 TESK2 1740 NRD1 1741 KTI12 1742 CC2D1B 1743 YIPF1 1744 JAK1 1745 SLC35D1 1746 DIRAS3 1747 ZZZ3 1748 GNG5 1749 ZNHIT6 1750 ODF2L 1751 SEP15 1752 BARHL2 1753 GCLM 1754 CLCC1//GPSM2//C1orf62 1755 SORT1 1756 SLC16A4 1757 PHTF1 1758 RSBN1 1759 DENND2C//BCAS2 1760 CD58 1761 SPAG17//WDR3 1762 REG4//NBPF7 1763 RP11- 1764 94I2.2//NBPF16// NBPF11//NBPF15// NBPF8//NBPF20// NBPF10//NBPF14// NBPF1//LOC100288142// NBPF12//KIAA1245// LOC100290137 APH1A 1765 POGZ 1766 TDRKH 1767 THEM4 1768 S100A11 1769 CRNN 1770 SPRR2C S100A12 1771 S100A8 1772 GATAD2B//PLIN2 1773 DENND4B 1774 PBXIP1 1775 PYGO2 1776 SHC1 1777 DCST2 1778 GBA//GBAP 1779 ASH1L 1780 RIT1 1781 MEF2D 1782 AIM2 1783 COPA 1784 DEDD 1785 TADA1L 1786 GPA33 1787 CD247 1788 F5 1789 PIGC 1790 KIAA0040 1791 TOR1AIP2//TOR1AIP1// 1792 IFRG15 STX6//KIAA1614 1793 EDEM3 1794 UCHL5 1795 DENND1B 1796 DDX59 1797 KIF21B 1798 ARL8A 1799 CYB5R1 1800 MYBPH 1801 CHI3L1 1802 PIK3C2B//LOC100130573 1803 NUAK2 1804 NUCKS1 1805 FAIM3 1806 PLXNA2 1807 SLC30A1 1808 LPGAT1 1809 ANGEL2 1810 RAB3GAP2//AURKAPS1// 1811 AURKA//SNORA36B TP53BP2 1812 NVL 1813 TMEM63A 1814 PARP1 1815 ITPKB 1816 TARBP1 1817 CHML 1818 AKT3 1819 SMYD3 1820 AHCTF1 1821 OR1C1 1822 NCOA1 1823 HADHB 1824 ABHD1//PREB 1825 SPAST 1826 SLC30A6//DDX50 1827 CRIPT 1828 MSH2 1829 FOXN2 1830 CCDC104 1831 VRK2 1832 AHSA2//USP34 1833 OTX1 1834 AFTPH 1835 CEP68 1836 PLEK 1837 ANXA4 1838 MXD1 1839 NAGK 1840 SMYD5//NOTO 1841 MTHFD2 1842 TTC31 1843 SEMA4F 1844 TMSB10 1845 SH2D6 1846 GNLY 1847 KCNIP3 1848 CNNM4 1849 CNNM3 1850 ZAP70 1851 LIPT1//MRPL30 1852 MAP4K4 1853 IL1R2 1854 IL1R1 1855 IL18R1 1856 POLR1B 1857 CHCHD5 1858 IL1RN 1859 PSD4 1860 DDX18 1861 INSIG2 1862 TMEM177//LOC100125918 1863 RALB 1864 PROC 1865 GPR17//LOC100291428// 1866 LIMS2 IMP4 1867 FAM123C 1868 ACVR2A 1869 MBD5 1870 LYPD6B 1871 SLC4A10 1872 UBR3 1873 HAT1 1874 ITGA6 1875 ZAK 1876 OSBPL6 1877 PLEKHA3 1878 ZC3H15 1879 COL3A1 1880 GLS 1881 OBFC2A 1882 COQ10B 1883 MARS2 1884 CFLAR 1885 NOP58 1886 FAM117B 1887 CYP20A1 1888 FASTKD2 1889 PIKFYVE 1890 C2orf62 1891 SLC11A1 1892 AGFG1 1893 CHRNG 1894 EIF4E2 1895 TRPM8 1896 LRRFIP1 1897 GAL3ST2 1898 TMEM18 1899 LAPTM4A 1900 SF3B14 1901 TP53I3 1902 UNQ2999 1903 GPR113//SELI 1904 MPV17 1905 PPM1G 1906 NLRC4 1907 CDC42EP3 1908 HNRPLL 1909 COX7A2L 1910 KCNG3 1911 CALM2//C2orf61 1912 BCL11A 1913 XPO1 1914 NAT8B 1915 DUSP11 1916 MOGS 1917 SNRNP200 1918 SEMA4C 1919 MITD1 1920 IL1A 1921 SLC35F5 1922 CCDC93 1923 CLASP1 1924 SAP130 1925 YSK4 1926 GTDC1 1927 ORC4L 1928 NR4A2//FLJ46875 1929 DPP4 1930 GALNT3 1931 SCN7A 1932 FRZB 1933 STK17B 1934 CLK1//PPIL3 1935 MPP4 1936 INO80D 1937 KLF7 1938 FAM119A 1939 NGEF 1940 ARL4C 1941 RAB17 1942 HDLBP 1943 LRRN1 1944 SETD5 1945 IRAK2 1946 C3orf42 1947 TSEN2 1948 NR2C2//MRPS25 1949 UBE2E1 1950 C3orf35 1951 SNRK 1952 ZNF197 1953 GNAI2 1954 ALAS1 1955 PRKCD 1956 CACNA1D 1957 PXK 1958 PTPRG 1959 ATXN7 1960 SLC35A5 1961 SLC15A2 1962 CCDC48 1963 DNAJC13 1964 CLDN18 1965 GYG1 1966 SELT 1967 MED12L 1968 RAP2B 1969 MYNN 1970 ABCF3 1971 VPS8 1972 HRG 1973 EIF4A2//SNORA4 1974 LPP 1975 CCDC50 1976 LOC152217 1977 TADA3L 1978 SEC13 1979 TIMP4 1980 METTL6 1981 DAZL//DAZ4//DAZ3// 1982 DAZ2 SATB1//TBC1D5 1983 SCN10A 1984 SEC22C 1985 ZDHHC3 1986 ZDHHC3 1987 SLC6A20 1988 UQCRC1 1989 PRKAR2A 1990 IMPDH2 1991 CCDC71 1992 UBA7 1993 CAMKV 1994 WDR82 1995 LMOD3 1996 FOXP1 1997 MORC1 1998 ATG3 1999 GSK3B//LOC100129275 2000 HCLS1 2001 KPNA1 2002 PTPLB 2003 C3orf22 2004 RPN1 2005 KIAA1257//ACAD9// 2006 LOC100132731 FOXL2 2007 MECOM 2008 PLD1 2009 GNB4 2010 MRPL47 2011 KLHL6 2012 THPO 2013 ETV5 2014 BCL6//LOC100131635 2015 ATP13A5 2016 TMEM44 2017 KIAA1530 2018 TACC3 2019 CNO 2020 BST1 2021 KLF3 2022 TMEM33//DCAF4L1 2023 KIT 2024 ENAM 2025 FAM47E//STBD1 2026 ENOPH1 2027 PDLIM5 2028 CCDC109B//HIGD1A// 2029 CCDC13 EGF 2030 PCDH10 2031 RAB33B 2032 TMEM184C 2033 RBM46 2034 GRIA2 2035 C4orf39 2036 KLHL2 2037 TLL1 2038 F11 2039 SLBP 2040 HAUS3//POLN 2041 PPARGC1A 2042 TLR10 2043 C4orf34 2044 TXK 2045 RPL21P44 KDR 2046 RCHY1 2047 CNOT6L 2048 PLAC8 2049 HPSE 2050 GPRIN3 2051 PPA2 2052 COL25A1 2053 C4orf3 2054 QRFPR 2055 MFSD8 2056 MAP9 2057 PDGFC 2058 TKTL2 2059 ACSL1 2060 SUB1//TMEM183A 2061 CARD6 2062 MCCC2 2063 TNPO1 2064 PDE8B 2065 PAPD4 2066 THBS4 2067 FAM151B 2068 RASGRF2 2069 SNX2 2070 LMNB1//PCIF1 2071 MEGF10 2072 LEAP2 2073 TCF7 2074 KDM3B 2075 CXXC5 2076 SLC4A9 2077 ANKHD1- 2078 EIF4EBP3//ANKHD1// EIF4EBP3 KIAA0141 2079 GRPEL2 2080 MFAP3 2081 GABRA6 2082 GABRA1 2083 DOCK2 2084 RANBP17//USP12 2085 ERGIC1 2086 ATP6V0E1//SNORA74B ZNF346 2087 NSD1 2088 CLPTM1L 2089 UGT3A1 2090 GDNF 2091 TTC33 2092 hCG_2039148 MOCS2 2093 SLC38A9 2094 CCDC125 2095 ANKRA2 2096 HAPLN1 2097 CCNH 2098 TMEM161B 2099 MBLAC2 2100 MCTP1 2101 TICAM2//TMED7// 2102 TMED7-TICAM2 KIF3A 2103 C5orf15 2104 SKP1 2105 CXCL14 2106 KLHL3 2107 CD14 2108 YIPF5 2109 LARS 2110 DCTN4 2111 CCDC69 2112 ATOX1 2113 TIMD4 2114 ADAM19 2115 SLIT3 2116 RNF44 2117 DOK3 2118 MGAT4B//SQSTM1 2119 C5orf45//SQSTM1 2120 RASGEF1C 2121 MGAT1 2122 IRF4 2123 HIVEP1 2124 E2F3 2125 HIST1H4I 2126 HIST1H2BM 2127 MOG 2128 ZNRD1//NCRNA00171 2129 TRIM15 2130 HCG27 2131 BAT2//SNORA38 2132 CYP21A2 2133 ITPR3 2134 MAPK14 2135 MAPK13 2136 PNPLA1 2137 SFRS3 2138 CDKN1A 2139 FOXP4 2140 CUL9 2141 RUNX2 2142 ZNF451 2143 SOBP 2144 C6orf182 2145 KIAA1919 2146 RWDD1 2147 KPNA5 2148 TPD52L1 2149 ARG1 2150 RAB32 2151 ARID1B 2152 SLC22A3 2153 SERPINB1 2154 C6orf146 2155 GCM2 2156 ATXN1 2157 DCDC2//KAAG1 2158 HIST1H3I 2159 HIST1H4L 2160 GABBR1 2161 RNA243 DDAH2 2162 CLIC1 2163 NEU1 2164 RXRB 2165 VPS52 2166 TCP11 2167 CLPS 2168 PGC 2169 ZNF318 2170 YIPF3 2171 MRPL14 2172 PLA2G7 2173 PKHD1 2174 IL17F 2175 HTR1B 2176 GABRR2 2177 UBE2J1 2178 BACH2 2179 MCM9 2180 VNN1 2181 IL20RA 2182 FLJ27255 2183 T 2184 RPS6KA2 2185 HGC6.3 2186 UNC84A//C7orf20 2187 SDK1 2188 ZDHHC4 2189 C7orf26 2190 GLCCI1//tcag7.903 2191 GPNMB 2192 CCDC126 2193 WIPF3//ZNRF2//LOC441208 2194 GPR141 2195 STARD3NL 2196 POU6F2 2197 CDC2L5 2198 ZMIZ2 2199 UPP1 2200 ZNF273 2201 KCTD7//RABGEF1 2202 RABGEF1//tcag7.967// 2203 tcag7.951//KCTD7// LOC100293333 CCDC132 2204 PVRIG//PILRB//STAG3 2205 PILRB//PVRIG//STAG3 2206 C7orf51 2207 GNB2 2208 LRRC17 2209 LRRN3 2210 CFTR 2211 LSM8 2212 LUC7L2 2213 MGAM//LOC100124692 2214 GIMAP7 2215 INSIG1 2216 RBM33 2217 ICA1 2218 FAM126A 2219 HIBADH 2220 TRIL 2221 SCRN1 2222 ELMO1 2223 INHBA 2224 CAMK2B 2225 NPC1L1 2226 DDC//LOC100129427 2227 NSUN5//NSUN5B// 2228 NSUN5C CLDN3 2229 C7orf23//DMTF1 2230 SRI 2231 BET1 2232 MCM7 2233 GATS 2234 ATXN7L1//RINT1// 2235 EFCAB10 KIAA1549 2236 SLC37A3 2237 SMARCD3 2238 MLL3//BAGE2 2239 CLN8 2240 MSRA 2241 PIWIL2 2242 NEFM//LOC100129717 2243 EPHX2 2244 LEPROTL1 2245 MAK16//C8orf41 2246 AP3M2 2247 FNTA 2248 SGK196 2249 UBE2V2 2250 FLJ46365 N/A SNTG1 2251 TRIM55 2252 C8orf45 2253 PREX2 2254 PLEKHF2 2255 BAALC//FLJ10489 2256 TTC35 2257 MTBP 2258 ZHX2 2259 RNF139 2260 TG 2261 DENND3//C8orf60 2262 TNFRSF10D 2263 TRIM35 2264 GSR 2265 WHSC1L1 2266 PCMTD1//PXDNL 2267 NCOA2 2268 TRAM1//LOC286190 2269 RUNX1T1 2270 EXT1 2271 DDEF1IT1 2272 CDC37L1 2273 UBE2R2 2274 UBAP1//KIF24 2275 GALT 2276 RGP1//GBA2 2277 TGFBR1 2278 C9orf6//IKBKAP 2279 IMAGE5303689 N/A ATP6V1G1 2280 TLR4 2281 SET 2282 MRPL41 2283 C9orf68 2284 HAUS6//SCARNA8 2285 KLHL9 2286 C9orf82 2287 NDUFB6//DFFB 2288 SIT1 2289 FAM108B1 2290 TRPM6 2291 FRMD3 2292 SLC28A3 2293 BICD2 2294 C9orf84 2295 AKNA 2296 MEGF9 2297 C5 2298 GOLGA1//SCAI 2299 SH2D3C 2300 FAM102A 2301 FLJ10232 N/A ASB6 2302 BAT2L 2303 EDF1 2304 FBXW5 2305 C10orf18 2306 FBXO18 2307 GATA3 2308 CUGBP2 2309 VIM 2310 STAM 2311 WAC 2312 BAMBI 2313 ZNF487//LOC439911 2314 ALOX5 2315 WDFY4 2316 SRGN 2317 CCDC109A 2318 FAM149B1//FAM149B2 2319 MINPP1 2320 PTEN//PTENP1 2321 ENTPD1//C10orf131 2322 ABCC2 2323 SFXN2 2324 SHOC2 2325 ACSL5 2326 BCCIP//DHX32 2327 FAM188A 2328 CUBN 2329 SVIL//hCG_1783494 2330 FAM13C//PHYHIPL 2331 ATAD1 2332 ANKRD22 2333 FLJ34077 N/A COX15 2334 ERLIN1 2335 ACTR1A 2336 ABLIM1 2337 RAB11FIP2 2338 C10orf84 2339 PRDX3 2340 C10orf119 2341 NSMCE4A 2342 TALDO1//INTS8 2343 TNNT3 2344 FXC1 2345 PDE3B 2346 DNAJC24 2347 PTPRJ//OR4B1 2348 C11orf31 2349 TMEM109 2350 CD6 2351 CD5 2352 TMEM138 2353 POLR2G 2354 TMEM179B 2355 NAT11 2356 OTUB1 2357 RBM14//RBM4 2358 AIP 2359 PPFIA1 2360 IL18BP//NUMA1 2361 C11orf30 2362 C11orf82 2363 TMEM126B 2364 C11orf73 2365 PIWIL4 2366 LOC100132686 2367 PAFAH1B2 2368 UBE4A 2369 TRAPPC4 2370 SC5DL 2371 VWA5A//OR10D1P 2372 STT3A 2373 VPS26B 2374 TRIM21 2375 ZBED5 2376 SAAL1 2377 FANCF 2378 LIN7C 2379 PHF21A 2380 CUGBP1 2381 OSBP 2382 CYBASC3 2383 TUT1 2384 SLC25A45 2385 LTBP3 2386 EIF1AD 2387 GAB2 2388 CREBZF 2389 PICALM 2390 SLC36A4 2391 CCDC82 2392 KIAA1826 2393 MPZL3 2394 MPZL2 2395 H2AFX 2396 SIAE 2397 ZBTB44 2398 HSN2 2399 ADIPOR2 2400 NCAPD2//SCARNA10// 2401 FADS1 PTPN6 2402 CLEC4D 2403 CDKN1B 2404 GOLT1B 2405 FAR2 2406 FGD4 2407 TMEM106C 2408 TMBIM6 2409 C12orf62 2410 PRR13//PCBP2 2411 DGKA 2412 COQ10A 2413 TSPAN31 2414 CDK4/MARCH9/C3HC4 2415 LEMD3 2416 IRAK3 2417 TMTC3 2418 ACTR6 2419 TCTN1 2420 PXMP2//PGAM5 2421 DCP1B 2422 SLC2A3//SLC2A14 2423 C3AR1 2424 PLBD1 2425 TM7SF3 2426 ASB8//PHB 2427 LMBR1L 2428 FMNL3//PRPF40B 2429 AAAS 2430 NFE2 2431 GPR84 2432 CD63 2433 SARNP//DNAJC14 2434 NACA 2435 CDK4//TSPAN31 2436 TMBIM4//LOC100133322 2437 IL22 2438 LIN7A 2439 HAL 2440 APPL2 2441 GLTP 2442 GIT2 2443 VPS29 2444 PPTC7 2445 DDX54//CCDC42B 2446 SLC24A6 2447 SDS 2448 RBM19 2449 MED13L 2450 C12orf49 2451 FBXO21 2452 WSB2 2453 TAOK3 2454 CIT 2455 RAB35 2456 RPLP0 2457 PXN 2458 TRIAP1 2459 SFRS9 2460 POP5 2461 UNQ1887 2462 C12orf43 2463 ANAPC5 2464 KDM2B 2465 MORN3 2466 TMEM120B//RHOF 2467 LOC338799 DIABLO//B3GNT4 2468 VPS33A 2469 CLIP1 2470 PITPNM2 2471 EIF2B1 2472 CCDC92 2473 NCOR2 2474 DHX37 2475 DDX51 2476 POLE 2477 GOLGA3 2478 ZMYM2 2479 SPATA13//C1QTNF9 2480 NUPL1 2481 PAN3//EEF1A1//CHCHD2 2482 ALOX5AP 2483 EEF1DP3 2484 KL 2485 UFM1 2486 NARG1L 2487 ITM2B 2488 FNDC3A 2489 CDADC1 2490 ARL11 2491 LMO7 2492 DNAJC3 2493 TM9SF2 2494 CLYBL 2495 PCCA 2496 ABHD13 2497 LAMP1 2498 TMCO3 2499 UPF3A 2500 ZMYM5//ZMYM2 2501 ZDHHC20//LOC728099 2502 PARP4 2503 MTMR6//LOC646482 2504 HSPH1 2505 N4BP2L2//CG030 2506 ELF1 2507 LCP1 2508 KPNA3 2509 C13orf1 2510 DLEU2//DLEU2L 2511 GUCY1B2 2512 INTS6 2513 DACH1 2514 TBC1D4 2515 EDNRB 2516 UGGT2 2517 GPR183 2518 LIG4 2519 ANKRD10 2520 RASA3 2521 RNASE2//LOC643332 2522 RPGRIP1 2523 IRF9 2524 TSSK4 2525 C14orf21 2526 SCFD1 2527 FANCM 2528 ABHD12B 2529 PTGDR 2530 FBXO34//KIAA0831 2531 C14orf101 2532 ACTR10 2533 ARID4A 2534 JKAMP 2535 HIF1A 2536 SYNE2 2537 EXD2 2538 SLC39A9 2539 SFRS5 2540 PCNX 2541 SIPA1L1//SNORD56B// 2542 LOC145474//LOC283567 YLPM1 2543 BATF 2544 FLVCR2//RPS24 2545 GPR65 2546 TDP1 2547 EVL 2548 ZNF839 2549 TDRD9 2550 INF2 2551 PLD4 2552 MTA1//LOC647310// 2553 LOC100128343 NDRG2 2554 DAD1//OR6J1 2555 SLC7A8 2556 IPO4 2557 TM9SF1 2558 ADCY4 2559 RIPK3 2560 EAPP 2561 BAZ1A 2562 NFKBIA 2563 SEC23A 2564 C14orf104 2565 C14orf138 2566 SOS2 2567 NIN 2568 PYGL 2569 CNIH 2570 DHRS7 2571 WDR89 2572 ACTN1 2573 NUMB 2574 C14orf43 2575 ABCD4 2576 KIAA0317 2577 NEK9 2578 ANGEL1 2579 SPTLC2 2580 SERPINA6 2581 DICER1 2582 BCL11B 2583 ANKRD9 2584 PPP1R13B 2585 AKT1 2586 BRF1 2587 TUBGCP5 2588 SNRPN* 2589 APBA2 2590 MTMR15//MTMR10 2591 RYR3 2592 BAHD1 2593 CHP 2594 JMJD7- 2595 PLA2G4B//JMJD7// PLA2G4B HAUS2 2596 C15orf63//SERF2 2597 B2M 2598 TRIM69 2599 PLDN 2600 SQRDL 2601 GALK2 2602 USP8 2603 GLDN 2604 MAPK6 2605 LACTB 2606 RAB8B 2607 APH1B 2608 USP3//LOC100130855 2609 SNX1 2610 LBXCOR1//PIAS1// 2611 CALML4 NEO1 2612 MPI 2613 FBXO22//FBXO22OS 2614 RCN2 2615 FAH 2616 IL16 2617 ABHD2 2618 SLCO3A1 2619 MCTP2 2620 MEF2A//LYSMD4 2621 NIPA2//CYFIP1 2622 HERC2//HERC2P2// 2623 HERC2P3//LOC440248 MTMR10//MTMR15 2624 C15orf24 2625 SLC12A6 2626 LPCAT4 2627 INO80 2628 OIP5 2629 ZFP106 2630 CDAN1 2631 SPG11//ISLR 2632 SPPL2A 2633 GNB5//LOC100129973 2634 MYO5A 2635 ARPP19 2636 RAB27A 2637 CCPG1//PIGB//DYX1C1 2638 BNIP2 2639 CA12 2640 FAM96A 2641 KIAA0101//CSNK1G1 2642 TLE3 2643 PARP6 2644 NPTN 2645 MAN2C1 2646 IMP3 2647 MTHFS 2648 ST20//C15orf37 2649 TMC3 2650 AP3B2 2651 C15orf40 2652 WDR73 2653 NTRK3 2654 DET1 2655 TM2D3 2656 WDR90 2657 RHOT2//FBXL16 2658 TMEM204 2659 CRAMP1L//HN1L 2660 MAPK8IP3 2661 TBL3 2662 TSC2 2663 KCTD5//PRO0461// 2664 PDPK1 CLUAP1 2665 DNASE1 2666 DNAJA3 2667 CP110 2668 C16orf62 2669 LYRM1 2670 METTL9 2671 EEF2K 2672 POLR3E 2673 PLK1 2674 PRKCB 2675 IL21R//LOC283888 2676 SULT1A2//SULT1A1 2677 ATXN2L 2678 LAT^(¶) 2679 KIF22 2680 MAZ 2681 CORO1A//LOC606724 2682 ITGAL 2683 SRCAP//SNORA30 2684 ZNF646//ZNF668 2685 C16orf67 2686 TMEM188 2687 LPCAT2 2688 CETP 2689 CKLF 2690 CMTM1//CKLF 2691 TMEM208 2692 CTCF 2693 THAP11 2694 NUTF2 2695 EDC4 2696 SLC7A6//SLC7A6OS 2697 PRMT7 2698 SNTB2 2699 VPS4A 2700 DDX19B//DDX19A 2701 CHST4 2702 HP//HPR 2703 PLCG2 2704 KLHL36 2705 KIAA0182 2706 BANP//RUNDC2C 2707 TRAPPC2L 2708 SPG7 2709 CDK10 2710 TCF25 2711 AFG3L1 2712 LUC7L 2713 AXIN1 2714 JMJD8 2715 LMF1 2716 UNKL 2717 UNKL 2718 CLCN7 2719 MRPS34 2720 RNPS1 2721 NLRC3 2722 TRAP1//DNASE1 2723 ADCY9 2724 CORO7 2725 C16orf72 2726 RRN3//LOC653390// 2727 LOC730092//LOC100131998 XYLT1//LYRM2//ZC3H11A 2728 DCUN1D3//LYRM1 2729 IGSF6//METTL9 2730 CDR2//RRN3//LOC100131998// 2731 LOC653390 COG7 2732 GGA2 2733 NSMCE1 2734 GTF3C1 2735 CCDC101//LOC388242 2736 C16orf54 2737 KCTD13 2738 SEPT1 2739 ZNF764//ZNF747 2740 C16orf58//LOC100128371 2741 ITFG1 2742 ABCC11//LONP2 2743 NUDT21 2744 BBS2//OGFOD1 2745 CSNK2A2 2746 GOT2 2747 FAM96B 2748 FHOD1//SLC9A5 2749 ATP6V0D1//LOC100132855 2750 GFOD2 2751 SLC12A4 2752 DPEP3 2753 DPEP2 2754 CHTF8//HAS3 2755 COG8//PDF 2756 TERF2 2757 AARS 2758 ST3GAL2 2759 VAC14//LOC100130894 2760 AP1G1 2761 WDR59 2762 CTRB2//CTRB1 2763 TAF1C//ADAD2 2764 FBXO31 2765 ZCCHC14 2766 FAM38A 2767 CENPBD1 2768 TIMM22 2769 RPA1 2770 DPH1//OVCA2 2771 SGSM2 2772 ARRB2 2773 LOC100130950 2774 DNAH2 2775 PIGL 2776 TRPV2 2777 MPRIP 2778 DRG2 2779 ALKBH5//FLJ13773 2780 SMCR7 2781 WSB1 2782 TAOK1 2783 CPD 2784 SUZ12P 2785 RNF135 2786 ZNF830 2787 TAF15 2788 GGNBP2 2789 LASP1 2790 PSMD3 2791 CDC6 2792 NBR2 2793 TMUB2 2794 MGC57346//C17orf69 2795 NSF//LOC728806 2796 GOSR2 2797 NPEPPS//TBC1D3F// 2798 LOC440434 KPNB1 2799 CDK5RAP3 2800 ATP5G1 2801 UBE2Z 2802 XYLT2//LOC100130580 2803 NOG 2804 DGKE 2805 AKAP1 2806 TMEM49//CLTC//MIR21 2807 CLTC 2808 CA4 2809 C17orf64 2810 DCAF7 2811 PITPNC1 2812 NOL11//SNORA38B 2813 MAP2K6 2814 COG1 2815 CD300A 2816 TMEM104 2817 MRPS7 2818 KIAA0195 2819 TSEN54 2820 LLGL2 2821 LOC100134934//CDK3 2822 MFSD11 2823 SEPT9 2824 TNRC6C 2825 TMC8 2826 ENGASE 2827 RPTOR 2828 GPS1 2829 FN3KRP 2830 TBCD 2831 GEMIN4 2832 GLOD4 2833 SLC43A2 2834 PRPF8 2835 SMG6//C17orf6 2836 METT10D//LOC284009 2837 SHPK 2838 TAX1BP3 2839 P2RX5 2840 MYBBP1A//SPNS2 2841 PELP1 2842 PFN1 2843 ZNF232 2844 DHX33 2845 DERL2 2846 NLRP1//LOC728392 2847 ASGR2 2848 NEURL4//GPS2//D4S234E 2849 ZBTB4 2850 TP53 2851 VAMP2 2852 PIK3R5 2853 ELAC2 2854 NCOR1//C20orf191// 2855 LOC100131704 ZNF287 2856 TOM1L2//LOC246315 2857 GRAP//SNORD3B- 2858 1//SNORD3B- 2//LOC400581 ALDOC 2859 SDF2 2860 RAB34 2861 PHF12 2862 NUFIP2 2863 OMG 2864 EVI2B 2865 C17orf66//RSL24D1 2866 SYNRG//LOC100131822 2867 PLXDC1 2868 CACNB1 2869 PGAP3 2870 MED24 2871 NR1D1//THRA 2872 CCR7 2873 STAT5B//STAT5A 2874 FAM134C 2875 VAT1 2876 DUSP3 2877 C17orf65//ASB16 2878 UBTF 2879 GPATCH8 2880 MAP3K14//LOC100133991 2881 OSBPL7 2882 SLC35B1 2883 TOB1 2884 COX11//TOM1L1 2885 VEZF1 2886 SFRS1//FLJ44342 2887 SEPT4 2888 MED13//LOC100129112 2889 LIMD2//MAP3K3 2890 STRADA 2891 FTSJ3 2892 CD79B 2893 ICAM2 2894 ERN1 2895 TEX2 2896 LRRC37A3//LRRC37A2// 2897 LRRC37A//ARL17P1// LRRC37A4// LOC100294335// LOC644397 GNA13 2898 WIPI1//ARSG 2899 FAM20A 2900 NAT9 2901 GGA3 2902 H3F3B//H3F3C 2903 EXOC7 2904 SFRS2 2905 TMC6//LOC100131096 2906 USP36 2907 CD7 2908 RAB31 2909 VAPA 2910 SEH1L 2911 HQ0644/PRO0644 2912 RNMT 2913 RNF138 2914 GALNT1 2915 ELP2 2916 PIK3C3 2917 SLC14A2 2918 ME2 2919 SERPINB2//SERPINB10 2920 ZNF407 2921 ZNF236 2922 NFATC1//LOC100127994 2923 ENOSF1//TYMS 2924 MYOM1 2925 AFG3L2 2926 ABHD3 2927 OSBPL1A 2928 CDH2 2929 DSC1 2930 PSTPIP2 2931 C18orf32 2932 MBD2//SNORA37 2933 PIGN 2934 TMX3 2935 PQLC1 2936 GZMM 2937 ARID3A 2938 CIRBP 2939 DAZAP1 2940 SPPL2B 2941 NFIC 2942 VAV1 2943 ARHGEF18//LOC100128573 2944 STXBP2//LOC554363// 2945 LOC100131801 C19orf59 2946 ZNF317 2947 ILF3 2948 SMARCA4 2949 PRKCSH 2950 IER2 2951 CCDC130 2952 DCAF15 2953 IL27RA 2954 KLF2 2955 SIN3B 2956 DDA1 2957 GTPBP3 2958 FAM129C 2959 FCHO1 2960 ARRDC2 2961 IFI30 2962 C19orf60 2963 CRTC1//MAML2 2964 RFXANK//MEF2B// 2965 LOC729991 ZNF101 2966 ZNF738 2967 ZNF257//ZNF492// 2968 ZNF99//ZNF98//LOC646864 C19orf2 2969 KIAA0355//FLJ21369 2970 USF2 2971 TMEM147 2972 LIN37//PSENEN 2973 C19orf55 2974 TBCB//POLR2I 2975 ZNF382 2976 ZNF568 2977 ZNF420 2978 ZNF383 2979 CCDC97 2980 ZNF574 2981 CD177 2982 ZNF230//ZNF222 2983 VASP 2984 GRWD1 2985 FLT3LG 2986 ZNF175 2987 NCRNA00085 PPP2R1A ZNF808//ZNF578// 2988 ZNF611 LENG8 2989 FCAR 2990 RPL28 2991 U2AF2 2992 LOC100288114//MGC9913 ZFP28 2993 ZNF460 2994 ZNF549 2995 ZNF211 2996 ZNF587//ZNF417 2997 ZNF274 2998 ZNF544 2999 ZNF8 3000 TRIM28 3001 C19orf6 3002 C19orf34 3003 GNG7 3004 AES 3005 EEF2//SNORD37 3006 PLIN5//LRG1 3007 PLIN3 3008 PTPRS 3009 SAFB2//SAFB 3010 RANBP3 3011 GTF2F1//LOC100130856 3012 XAB2 3013 ELAVL1 3014 ADAMTS10 3015 FBXL12 3016 DNMT1 3017 TYK2 3018 KEAP1 3019 KRI1 3020 TMEM205//hCG_29977 3021 ZNF563 3022 MAN2B1//MORG1 3023 C19orf56 3024 DHPS 3025 TNPO2//SNORD41 3026 LPHN1 3027 NDUFB7 3028 AKAP8 3029 AKAP8L 3030 CHERP//C19orf44// 3031 CALR3 INSL3//JAK3 3032 IL12RB1 3033 UPK1A 3034 TYROBP 3035 ZNF529 3036 ZNF461 3037 ZNF607 3038 YIF1B 3039 PRR13 3040 CEACAM4 3041 PLAUR 3042 TRAPPC6A 3043 ERCC1//CD3EAP 3044 RTN2 3045 SYMPK 3046 PGLYRP1 3047 NOSIP 3048 PNKP 3049 NKG7 3050 FPR1 3051 ZNF28 3052 OSCAR 3053 MBOAT7 3054 LILRA5 3055 LILRA4 3056 ZNF550//ZNF549 3057 ZNF416 3058 ZNF256 3059 ZNF329 3060 FAM110A 3061 ITPA 3062 CDC25B 3063 CDS2 3064 CRLS1 3065 CSRP2BP 3066 SEC23B 3067 SLC24A3 3068 HCK 3069 ASXL1 3070 ACSS2 3071 C20orf4 3072 TGIF2 3073 C20orf24//SLA2 3074 RPN2//EEF1A2 3075 CTNNBL1 3076 ACTR5 3077 PPP1R16B 3078 DHX35 3079 PLCG1 3080 MYBL2 3081 SYS1//SYS1- 3082 DBNDD2//DBNDD2 DNTTIP1 3083 CTSA 3084 MMP9//LOC100128028 3085 DDX27 3086 SLC9A8 3087 RNF114 3088 PTPN1 3089 TSHZ2 3090 PFDN4 3091 CSTF1 3092 CASS4 3093 GNAS 3094 C20orf177 3095 CDH26 3096 C20orf197 3097 LOC284757 ARFGAP1 3098 PRPF6 3099 NSFL1C 3100 SIRPD 3101 SIRPG//SIRPA 3102 RNF24 3103 RASSF2 3104 TMX4 3105 JAG1 3106 C20orf74 3107 C20orf3 3108 C20orf112 3109 CDK5RAP1 3110 AHCY 3111 GGT7 3112 EDEM2 3113 RBM39//LOC643167 3114 BLCAP 3115 SERINC3//TTPAL 3116 ZNF335 3117 ELMO2 3118 B4GALT5 3119 DPM1 3120 ZFP64 3121 ZNF217 3122 CTSZ 3123 SYCP2 3124 PSMA7 3125 DIDO1 3126 YTHDF1 3127 CHODL 3128 BACH1 3129 C21orf41//BACH1 3130 IL10RB 3131 IFNAR1 3132 IFNGR2 3133 SON 3134 MORC3//DOPEY2 3135 DYRK1A 3136 KCNJ15 3137 ETS2 3138 RRP1B 3139 PFKL 3140 TRPM2 3141 ADARB1 3142 SAMSN1//LOC388813 3143 N6AMT1 3144 SYNJ1 3145 TMEM50B 3146 KCNE1 3147 PRDM15 3148 C2CD2 3149 WDR4 3150 U2AF1 3151 CSTB 3152 UBE2G2//SUMO3 3153 PTTG1IP 3154 POFUT2 3155 MCM3AP 3156 IL17RA//CECR7 3157 C22orf37 3158 LZTR1 3159 PPIL2//YPEL1 3160 CYTSA 3161 SNRPD3//C22orf13 3162 NF2 3163 LIMK2 3164 SLC5A1 3165 MCM5 3166 NCF4 3167 GGA1 3168 SH3BP1//PDXP 3169 POLR2F//LOC100131530 3170 APOBEC3A//APOBEC3B 3171 APOBEC3D 3172 ATF4 3173 CACNA1I 3174 ZC3H7B 3175 CCDC134 3176 TSPO 3177 NUP50 3178 TBC1D22A//LOC100289878 3179 RP3-402G11.5 3180 SAPS2 3181 NCAPH2 3182 BID 3183 SLC25A1 3184 KLHL22//KRT18 3185 PI4KA//PI4KAP1// 3186 PI4KAP2//LOC100293141 MAPK1 3187 ZNF70 3188 TPST2 3189 SF3A1//CCDC157 3190 PES1 3191 PIK3IP1 3192 PATZ1 3193 C22orf30 3194 IL2RB 3195 CSNK1E//LOC400927 3196 UNC84B 3197 CBX7//LOC100128400 3198 RPS19BP1 3199 MKL1//KIAA1659 3200 RANGAP1 3201 TCF20 3202 LDOC1L 3203 UNQ6126 3204 TUBGCP6 3205 SBF1//SBF1P1 3206 MSL3 3207 MOSPD2 3208 BMX//HNRPDL 3209 PDHA1 3210 YY2 3211 PDK3 3212 GK//GK3P//FTL//LOC652904 3213 CXorf59 3214 ATP6AP2 3215 USP9X//USP9Y 3216 RP2 3217 USP11 3218 RBM3 3219 FTSJ1 3220 WAS 3221 PLP2 3222 TSPYL2//GPR173 3223 MAGED2 3224 UBQLN2 3225 NLGN3 3226 ACRC 3227 UPRT 3228 CXorf26 3229 ATP7A 3230 DIAPH2 3231 CSTF2//RAD21 3232 ARMCX3 3233 ARMCX5 3234 GPRASP1 3235 TMEM31 3236 TBC1D8B 3237 MID2 3238 DOCK11 3239 LONRF3 3240 UBE2A 3241 SH2D1A 3242 OCRL 3243 SLC25A14 3244 HPRT1 3245 CD40LG 3246 AFF2 3247 SSR4//IDH3G 3248 FAM50A 3249 DKC1//SNORA36A// 3250 SNORA56 ARSD 3251 KAL1 3252 CTPS2 3253 RPS6KA3 3254 BCOR 3255 MAOB//NAT13 3256 ZNF41 3257 OTUD5 3258 KCND1 3259 ZMYM3 3260 MAGT1 3261 BRWD3 3262 TRMT2B 3263 GLA 3264 MORF4L2 3265 PSMD10 3266 ACSL4 3267 LAMP2 3268 CUL4B 3269 ODZ1 3270 ELF4 3271 RAP2C 3272 FAM127B//FAM127C// 3273 FAM127A TMEM185A 3274 ARD1A 3275 IRAK1 3276 DNASE1L1//RPL10 3277 SH3KBP1 3278 Mitochondrial N/A Mitochondrial N/A CCNL2 3279 INPP5B 3280 TLR5 3281 ADRB3//GOT1L1 3282 NOC2L//SAMD11// 3283 LOC401010 SHFM1 3284 ^(§) HUGO Gene Nomenclature Committee ^(¶) Synonymous with SPNS1//NPIPL2//LOC728741//LOC730153//NPIPL3//SPIN1//LOC728888//LOC100289169//LOC728734//LOC729602//LOC100288442//LOC100288332 *Synonymous with SNURF//IPW//SNORD116-16//SNORD116-18//SNORD116-21//SNORD116-22//SNORD116-17//SNORD116-19//PAR5//PAR-SN//SNORD116-2//SNORD116-25//SNORD116-26//SNORD107//SNORD115-12//SNORD115-5//SNORD115-6//SNORD115-9//SNORD116-11//SNORD116-12//SNORD116-13//SNORD116-28//SNORD116-4//SNORD64//PAR1//SNORD109A//SNORD109B//SNORD116-6//SNORD116-3//SNORD116-9//SNORD115-13//SNORD115-1//SNORD115-14//SNORD115-15//SNORD115-21//SNORD115-10//SNORD115-7//SNORD115-16//SNORD115-40//SNORD115-42//SNORD115- 11//SNORD115-29//SNORD115-34//SNORD115-36//SNORD115-4//SNORD115-43//HBII-52-24//SNORD116-5//SNORD116-7//SNORD115-26//SNORD115-30//SNORD116-15//SNORD116-8//SNORD115-2//SNORD115-39//SNORD116-14//SNORD116-20//SNORD115-8//SNORD115-3//SNORD115-38//SNORD115-41//SNORD115-22//SNORD115-44//SNORD116-1//SNORD115-17//SNORD115-18//SNORD115-19//SNORD115-20//SNORD116@ 

The claims defining the invention are as follows:
 1. A composition comprising a mixture of a DNA polymerase, whole peripheral blood leukocyte cDNA from a subject with a clinical sign of systemic inflammatory response syndrome (SIRS), wherein the whole peripheral blood leukocyte cDNA comprises a basic leucine zipper transcription factor (BATF) cDNA and a retinitis pigmentosa GTPase regulator interacting protein 1 (RPGRIP1) cDNA and the composition further comprises for each of up to 6 cDNAs, which comprise the BATF cDNA and RPGRIP1 cDNA, (1) two oligonucleotide primers that hybridize to opposite complementary strands of the cDNA, and (2) an oligonucleotide probe that comprises a reporter molecule and that hybridizes to the cDNA, wherein the oligonucleotide probe is a real-time polymerase chain reaction probe. 